The document summarizes the MPQA project which investigated recognizing and organizing opinions expressed in text. The project developed a framework for annotating perspectives in documents, training machine learning models to identify perspectives, and using perspective information to cluster passages for question answering applications. Initial experiments found annotator agreement of 85% for direct opinions and 50% for indirect opinions. A simple classifier achieved 66.4% accuracy in identifying direct opinions, outperforming the baseline. Clustering results using perspective information were mixed, helping organize answers for some topics but not others.
Sabrina is a PhD student interested in studying agile software development teams. She needs to select a research method but is unfamiliar with the options. Dr. Who recommends Grounded Theory (GT) as a way to generate a new theory by collecting qualitative data from practitioners. However, Sabrina finds the GT literature complex. The patterns in this document provide an overview of GT procedures to help make it more accessible for software engineering researchers. They describe how to get started with GT by reading key books and examples, applying for ethics approval to collect data, and avoiding an initial hypothesis to allow theory to emerge from the data.
This document summarizes the results of fact-finding interviews conducted with 16 planners to understand how they currently experience and interact with past customer interview artifacts ("artifacts"), and what they want from such artifacts. The interviews found that while most planners were aware of some artifacts, they felt much of the past interview content was stranded and difficult to find. Additionally, most planners found the current format of artifacts did not fully meet their needs. Based on these findings, the author developed a prototype system to better present artifacts using alternative visualizations, which were then tested with planners to evaluate performance.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
This document discusses using sentiment analysis to predict project performance by analyzing language in project reports and communications. It proposes focusing the analysis on select correspondence between key project members, periodic structured reports containing issues/risks, and narrative management reports. Conducting a narrow sentiment analysis of reliable, high-confidence data sources from within the project domain can improve predictive capabilities over broad analyses by increasing the signal-to-noise ratio and computational efficiency. The meaning of words can depend on context, so sentiment analysis may need to consider the applicable contexts more narrowly when including a broader range of project text.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
This document presents a project report for a Master's thesis on opinion mining and sentiment analysis. The report includes an abstract, acknowledgments, table of contents, and chapters covering the project overview and background on opinion mining, sentiment analysis, the project requirements and architecture, relevant technologies, the project design and implementation, approaches to sentiment analysis, and conclusions. The project aims to classify user comments from a major social site based on sentiment analysis.
The document provides guidance on how to frame a good query for a solution exchange community. It discusses including context about the query poster's work, clearly stating the issue being faced, specifically asking a question, and providing a signature. It also covers types of queries like seeking experiences, examples, referrals, or advice. Tips are given for drafting queries that will appeal to members and generate useful responses, such as focusing on real problems and avoiding ambiguity.
Sabrina is a PhD student interested in studying agile software development teams. She needs to select a research method but is unfamiliar with the options. Dr. Who recommends Grounded Theory (GT) as a way to generate a new theory by collecting qualitative data from practitioners. However, Sabrina finds the GT literature complex. The patterns in this document provide an overview of GT procedures to help make it more accessible for software engineering researchers. They describe how to get started with GT by reading key books and examples, applying for ethics approval to collect data, and avoiding an initial hypothesis to allow theory to emerge from the data.
This document summarizes the results of fact-finding interviews conducted with 16 planners to understand how they currently experience and interact with past customer interview artifacts ("artifacts"), and what they want from such artifacts. The interviews found that while most planners were aware of some artifacts, they felt much of the past interview content was stranded and difficult to find. Additionally, most planners found the current format of artifacts did not fully meet their needs. Based on these findings, the author developed a prototype system to better present artifacts using alternative visualizations, which were then tested with planners to evaluate performance.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
This document discusses using sentiment analysis to predict project performance by analyzing language in project reports and communications. It proposes focusing the analysis on select correspondence between key project members, periodic structured reports containing issues/risks, and narrative management reports. Conducting a narrow sentiment analysis of reliable, high-confidence data sources from within the project domain can improve predictive capabilities over broad analyses by increasing the signal-to-noise ratio and computational efficiency. The meaning of words can depend on context, so sentiment analysis may need to consider the applicable contexts more narrowly when including a broader range of project text.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
This document presents a project report for a Master's thesis on opinion mining and sentiment analysis. The report includes an abstract, acknowledgments, table of contents, and chapters covering the project overview and background on opinion mining, sentiment analysis, the project requirements and architecture, relevant technologies, the project design and implementation, approaches to sentiment analysis, and conclusions. The project aims to classify user comments from a major social site based on sentiment analysis.
The document provides guidance on how to frame a good query for a solution exchange community. It discusses including context about the query poster's work, clearly stating the issue being faced, specifically asking a question, and providing a signature. It also covers types of queries like seeking experiences, examples, referrals, or advice. Tips are given for drafting queries that will appeal to members and generate useful responses, such as focusing on real problems and avoiding ambiguity.
The document discusses the query formulation process in information retrieval systems. It defines a query and explains that query formulation involves refining the original query entered by the user, such as through tokenization, normalization, and stemming of terms. This refinement stage is followed by a structural alteration stage where the query is segmented and expanded with related concepts. Effective query formulation improves search quality by better representing the user's intent.
A scalable, lexicon based technique for sentiment analysisijfcstjournal
Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased
interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much
social media available on the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data
available now a days. The main focus of the research was to find such a technique that can efficiently
perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large
data set of tweets using Hadoop and the performance of the technique was measured in form of speed and
accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
FEATURE SELECTION AND CLASSIFICATION APPROACH FOR SENTIMENT ANALYSISmlaij
The document describes a proposed model for sentiment analysis of movie reviews using natural language processing and machine learning approaches. The model first applies various data pre-processing techniques to the dataset, including tokenization, pruning, filtering tokens, and stemming. It then investigates the performance of classifiers like Naive Bayes and SVM combined with different feature selection schemes, including term occurrence, binary term occurrence, term frequency and TF-IDF. Experiments are run using n-grams up to 4-grams to determine the best approach for sentiment analysis.
Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
IRJET- The Sentimental Analysis on Product Reviews of Amazon Data using the H...IRJET Journal
This document summarizes a research paper that analyzes sentiment on product reviews from Amazon using a hybrid approach. The researchers collected a dataset from the Amazon API and performed preprocessing including stemming, error correction, and stop word removal. They used n-gram analysis to extract features and defined positive, negative, and neutral words. SentiWordNet was used to determine sentiment polarities. A k-nearest neighbor classifier called WDE-KNN was trained on the dataset and used to classify sentiments into positive, negative or neutral. The researchers conducted experiments using different training-testing splits and found that KNN achieved higher accuracy than SVM, with up to 85.32% accuracy when the training and testing data was split 50-50.
This document summarizes several approaches for sentiment analysis of tweets. It discusses basic machine learning approaches using features like n-grams, part-of-speech tags, and relationships between tweets. Advanced approaches exploit social and topical contexts, learn sentiment-specific word embeddings, and use recursive neural networks and convolutional neural networks. Deep learning methods like recursive neural tensor networks and convolutional neural networks achieved state-of-the-art performance. Open challenges remain in handling sarcasm, ambiguity and incorporating contextual information.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
This document provides an introduction to text analytics using IBM SPSS Modeler. It defines key terms related to text analytics and outlines the main steps in the text analytics process: extraction, categorization, and visualization. It then provides a tutorial on using IBM SPSS Modeler to perform text analytics, including sourcing text, extracting concepts and relationships, categorizing records, and visualizing results. Templates and resources are described that can be used to start an interactive workbench session in Modeler for exploring text analytics.
Sentiment Analysis and Classification of Tweets using Data MiningIRJET Journal
This document summarizes research on using data mining techniques to perform sentiment analysis on tweets. The researchers collected tweets from Twitter and preprocessed the text to make it usable for building sentiment classifiers. They used three classifiers - K-Nearest Neighbor, Naive Bayes, and Decision Tree - and compared the results to determine which provided the best accuracy. Rapid Miner tool was used to preprocess the text, build the classifiers, and analyze the results. The goal was to determine people's sentiments expressed in their tweets and correctly classify them.
The document discusses using social network data, specifically tweets, to predict stock market movements. It outlines the general methodology, which includes collecting tweet data from APIs, filtering relevant tweets, preprocessing the text through normalization, noise removal, and feature extraction. Topic modeling and sentiment analysis are then used to extract topics and sentiment from tweets. These extracted features along with tweet metadata are then used to construct prediction models using classifiers like SVM and linear regression. The models are trained and tested using windowing to correlate sentiment and topic features from past tweets to subsequent stock price movements. Accuracy of these predictions and future areas of improvement are also discussed.
This document proposes a model to estimate overall sentiment score by applying rules of inference from discrete mathematics. It discusses sentiment analysis and related work using techniques like supervised/unsupervised learning. The problem is identifying sentiment components and restricting patterns for feature identification. Most approaches focus on nouns/adjectives but not verbs/adverbs. The model preprocesses product review datasets using NLTK for stemming, parsing and tokenizing. It builds a lexicon dictionary of positive and negative words. The Lexical Pattern Sentiment Analysis algorithm uses both lexicon and pattern mining - it selects sentence patterns, checks for positive/negative words in the lexicon, and calculates an overall sentiment score.
1) The document discusses text analytics and sentiment analysis, explaining that these tools are important for businesses to make better data-driven decisions based on customer feedback and opinions expressed online.
2) It covers different approaches to sentiment analysis such as using natural language processing (NLP) to identify concepts and attributes, and data mining techniques that represent text as numeric vectors that can be modeled.
3) The benefits and drawbacks of the NLP and data mining approaches are compared, noting that NLP provides more control and interpretability while data mining may achieve better predictive performance.
This literature review analyzed 24 empirical studies on gamification. It found that gamification provides mostly positive effects, though results depend on context and users. Motivational affordances like points, leaderboards, and badges were most commonly studied. Outcomes examined included psychological factors like motivation and engagement, as well as behavioral measures like activity and productivity. The majority of studies reported some positive effects of gamification, though not all effects were sustained long-term. Both benefits and criticisms of gamification were identified. A wide range of contexts were examined, most often education/learning. While gamification seemed to increase engagement in many cases, the review indicated it may not be equally effective in all situations.
This document discusses improving search literacy to help users learn as they search for technical solutions. The authors:
1) Analyzed questions and answers on Stack Overflow to understand features that make questions easy to answer, such as providing context and details. This revealed skills like properly formatting questions that help learning.
2) Propose search interface designs inspired by Stack Overflow, such as prompting users for more question details, using dialogue to elicit more information, and allowing users to explore definitions of key terms.
3) The goal is to design search engines that help users learn search skills and about technical domains as they search for solutions, similar to how asking questions on Stack Overflow supports learning.
The document discusses two neural network models for reading comprehension tasks: the Attentive Reader model proposed by Herman et al. in 2015 and the Stanford Reader model proposed by Chen et al. in 2016. The author implemented a two-layer attention model inspired by these previous models that achieves a 1.5% higher accuracy on reading comprehension tasks compared to the Stanford Reader.
Efficient Refining Of Why-Not Questions on Top-K Queriesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
The document summarizes key concepts in reinforcement learning:
- Agent-environment interaction is modeled as states, actions, and rewards
- A policy is a rule for selecting actions in each state
- The return is the total discounted future reward an agent aims to maximize
- Tasks can be episodic or continuing
- The Markov property means the future depends only on the present state
- The agent-environment framework can be modeled as a Markov decision process
This thesis examines using machine learning methods to detect malfunctions in Road Weather Information System (RWIS) sensors. The author builds statistical models using weather data from RWIS and other sensors to predict temperature, precipitation, and visibility values. Significant deviations between predicted and actual sensor values would indicate malfunctions. Classification, regression, and Hidden Markov Models are applied. Experiments show Least Median Square and M5P regression accurately predict temperature and visibility. Decision trees and Bayesian networks perform well for precipitation. Hidden Markov Models also accurately predict temperature classes.
The document discusses the query formulation process in information retrieval systems. It defines a query and explains that query formulation involves refining the original query entered by the user, such as through tokenization, normalization, and stemming of terms. This refinement stage is followed by a structural alteration stage where the query is segmented and expanded with related concepts. Effective query formulation improves search quality by better representing the user's intent.
A scalable, lexicon based technique for sentiment analysisijfcstjournal
Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased
interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much
social media available on the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data
available now a days. The main focus of the research was to find such a technique that can efficiently
perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large
data set of tweets using Hadoop and the performance of the technique was measured in form of speed and
accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
FEATURE SELECTION AND CLASSIFICATION APPROACH FOR SENTIMENT ANALYSISmlaij
The document describes a proposed model for sentiment analysis of movie reviews using natural language processing and machine learning approaches. The model first applies various data pre-processing techniques to the dataset, including tokenization, pruning, filtering tokens, and stemming. It then investigates the performance of classifiers like Naive Bayes and SVM combined with different feature selection schemes, including term occurrence, binary term occurrence, term frequency and TF-IDF. Experiments are run using n-grams up to 4-grams to determine the best approach for sentiment analysis.
Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
IRJET- The Sentimental Analysis on Product Reviews of Amazon Data using the H...IRJET Journal
This document summarizes a research paper that analyzes sentiment on product reviews from Amazon using a hybrid approach. The researchers collected a dataset from the Amazon API and performed preprocessing including stemming, error correction, and stop word removal. They used n-gram analysis to extract features and defined positive, negative, and neutral words. SentiWordNet was used to determine sentiment polarities. A k-nearest neighbor classifier called WDE-KNN was trained on the dataset and used to classify sentiments into positive, negative or neutral. The researchers conducted experiments using different training-testing splits and found that KNN achieved higher accuracy than SVM, with up to 85.32% accuracy when the training and testing data was split 50-50.
This document summarizes several approaches for sentiment analysis of tweets. It discusses basic machine learning approaches using features like n-grams, part-of-speech tags, and relationships between tweets. Advanced approaches exploit social and topical contexts, learn sentiment-specific word embeddings, and use recursive neural networks and convolutional neural networks. Deep learning methods like recursive neural tensor networks and convolutional neural networks achieved state-of-the-art performance. Open challenges remain in handling sarcasm, ambiguity and incorporating contextual information.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
This document provides an introduction to text analytics using IBM SPSS Modeler. It defines key terms related to text analytics and outlines the main steps in the text analytics process: extraction, categorization, and visualization. It then provides a tutorial on using IBM SPSS Modeler to perform text analytics, including sourcing text, extracting concepts and relationships, categorizing records, and visualizing results. Templates and resources are described that can be used to start an interactive workbench session in Modeler for exploring text analytics.
Sentiment Analysis and Classification of Tweets using Data MiningIRJET Journal
This document summarizes research on using data mining techniques to perform sentiment analysis on tweets. The researchers collected tweets from Twitter and preprocessed the text to make it usable for building sentiment classifiers. They used three classifiers - K-Nearest Neighbor, Naive Bayes, and Decision Tree - and compared the results to determine which provided the best accuracy. Rapid Miner tool was used to preprocess the text, build the classifiers, and analyze the results. The goal was to determine people's sentiments expressed in their tweets and correctly classify them.
The document discusses using social network data, specifically tweets, to predict stock market movements. It outlines the general methodology, which includes collecting tweet data from APIs, filtering relevant tweets, preprocessing the text through normalization, noise removal, and feature extraction. Topic modeling and sentiment analysis are then used to extract topics and sentiment from tweets. These extracted features along with tweet metadata are then used to construct prediction models using classifiers like SVM and linear regression. The models are trained and tested using windowing to correlate sentiment and topic features from past tweets to subsequent stock price movements. Accuracy of these predictions and future areas of improvement are also discussed.
This document proposes a model to estimate overall sentiment score by applying rules of inference from discrete mathematics. It discusses sentiment analysis and related work using techniques like supervised/unsupervised learning. The problem is identifying sentiment components and restricting patterns for feature identification. Most approaches focus on nouns/adjectives but not verbs/adverbs. The model preprocesses product review datasets using NLTK for stemming, parsing and tokenizing. It builds a lexicon dictionary of positive and negative words. The Lexical Pattern Sentiment Analysis algorithm uses both lexicon and pattern mining - it selects sentence patterns, checks for positive/negative words in the lexicon, and calculates an overall sentiment score.
1) The document discusses text analytics and sentiment analysis, explaining that these tools are important for businesses to make better data-driven decisions based on customer feedback and opinions expressed online.
2) It covers different approaches to sentiment analysis such as using natural language processing (NLP) to identify concepts and attributes, and data mining techniques that represent text as numeric vectors that can be modeled.
3) The benefits and drawbacks of the NLP and data mining approaches are compared, noting that NLP provides more control and interpretability while data mining may achieve better predictive performance.
This literature review analyzed 24 empirical studies on gamification. It found that gamification provides mostly positive effects, though results depend on context and users. Motivational affordances like points, leaderboards, and badges were most commonly studied. Outcomes examined included psychological factors like motivation and engagement, as well as behavioral measures like activity and productivity. The majority of studies reported some positive effects of gamification, though not all effects were sustained long-term. Both benefits and criticisms of gamification were identified. A wide range of contexts were examined, most often education/learning. While gamification seemed to increase engagement in many cases, the review indicated it may not be equally effective in all situations.
This document discusses improving search literacy to help users learn as they search for technical solutions. The authors:
1) Analyzed questions and answers on Stack Overflow to understand features that make questions easy to answer, such as providing context and details. This revealed skills like properly formatting questions that help learning.
2) Propose search interface designs inspired by Stack Overflow, such as prompting users for more question details, using dialogue to elicit more information, and allowing users to explore definitions of key terms.
3) The goal is to design search engines that help users learn search skills and about technical domains as they search for solutions, similar to how asking questions on Stack Overflow supports learning.
The document discusses two neural network models for reading comprehension tasks: the Attentive Reader model proposed by Herman et al. in 2015 and the Stanford Reader model proposed by Chen et al. in 2016. The author implemented a two-layer attention model inspired by these previous models that achieves a 1.5% higher accuracy on reading comprehension tasks compared to the Stanford Reader.
Efficient Refining Of Why-Not Questions on Top-K Queriesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
The document summarizes key concepts in reinforcement learning:
- Agent-environment interaction is modeled as states, actions, and rewards
- A policy is a rule for selecting actions in each state
- The return is the total discounted future reward an agent aims to maximize
- Tasks can be episodic or continuing
- The Markov property means the future depends only on the present state
- The agent-environment framework can be modeled as a Markov decision process
This thesis examines using machine learning methods to detect malfunctions in Road Weather Information System (RWIS) sensors. The author builds statistical models using weather data from RWIS and other sensors to predict temperature, precipitation, and visibility values. Significant deviations between predicted and actual sensor values would indicate malfunctions. Classification, regression, and Hidden Markov Models are applied. Experiments show Least Median Square and M5P regression accurately predict temperature and visibility. Decision trees and Bayesian networks perform well for precipitation. Hidden Markov Models also accurately predict temperature classes.
This document provides instructions for a machine learning lab assignment. Students are asked to use the Weka machine learning tool to classify RNA-binding proteins using various algorithms, including Naive Bayes, J48 decision tree, SVM with linear and RBF kernels. Performance is measured using 5-fold cross-validation on the training set and classification of a separate test protein. Results for accuracy and other metrics are recorded in tables.
Sentiment Analysis in Social Media and Its OperationsIRJET Journal
This document summarizes a literature review on sentiment analysis in social media. It explores the styles, platforms, and applications of sentiment analysis. Most papers used either a dictionary-based approach or machine learning approach to analyze sentiment in social media text, with some combining both. Twitter was the most common social media platform used to collect data due to its large volume of public posts. Sentiment analysis has been applied in various domains including business, politics, health, and tracking world events. It can provide valuable insights for organizations and help improve products, services, and decision making.
Systematic Literature Reviews and Systematic Mapping Studiesalessio_ferrari
Lecture slides on Systematic Literature Reviews and Systematic Mapping Studies in software engineering. It describes the different steps, discusses differences between the two methods, and gives guidelines on how to conduct these types of study.
The Role of Families and the Community Proposal Template (N.docxssusera34210
The Role of Families and the Community Proposal Template
(
Name of Presenter:
Focus of proposed presentation:
Age group your proposal will focus on:
)
Proposal Directions: Please complete each of the following sections of the proposal in order to demonstrate your competency in the area of the role that families and the community play in promoting optimal cognitive development. In each box, address the topic that is presented. The space for sharing your knowledge will expand with your text, so please do not feel limited by the space that is currently showing.
Explain how theory can influence the choices parents make when promoting their child’s cognitive development abilities for your chosen age group. Use specific examples from one theory of cognitive development that has been discussed this far in the course.
Explain how the environment that families create at home helps promote optimal cognitive development for your chosen age group. Provide at least two strategies that you would encourage parents to foster this type of environment.
Discuss the role that family plays in developing executive functions for your chosen age group. Provide at least two strategies that you suggest parents use to help foster the development of executive functions.
Examine the role that family plays in memory development for your chosen age group. Provide at least strategies parents can use to support memory development.
Examine the role that family plays in conceptual development for your chosen age group. Use ideas from your response to the Week 3 Discussion 1 forum to provide at least two strategies families can use to support development in this area.
Explain at least two community resources that would suggest families use to support the cognitive development of their children for your chosen age group.
Analyze of the role that you would play in helping to support families within your community to promote optimal cognitive development for your chosen age group.
Running Head: MINI-PROJECT: QUALITATIVE ANALYSIS 1
MINI-PROJECT: QUALITATIVE ANALYSIS 6
Mini-Project: Qualitative Analysis
Student’s Name
Institutional Affiliation
MINI-PROJECT: QUALITATIVE ANALYSIS
Introduction
It is important for qualitative data to be analyzed and the themes that emerge identified so that the data can be presented in a way that is understandable. Theme identification is an essential task in qualitative research and themes could mean abstract, often fuzzy, constructs which investigators identify before, during, and after data collection. I will discuss the themes that emerge from the data collected from the interview.Analyzing and presenting qualitative data in an understandable manner is a five step procedure that I will also explain in this paper.
Emergi ...
A guide to deal with uncertainties in software project managementijcsit
Various project management approaches do not consider the impact that uncertainties have on the project.
The identified threats by uncertainty in a projec day-to-day are real and immediate and the expectations in
a project are often high. The project manager faces a dilemma: decisions must be made in the present
about future situations which are inherently uncertain. The use of uncertainty management in project can
be a determining factor for the project success. This paper presents a systematic review about uncertainties
management in software projects and a guide is proposed based on the review. It aims to present the best
practices to manage uncertainties in software projects in a structured way including techniques and
strategies to uncertainties containment.
IRJET-Classifying Mined Online Discussion Data for Reflective Thinking based ...IRJET Journal
This document presents a methodology for classifying mined online discussion data to identify reflective thinking based on ontology. It involves the following steps:
1. Collecting online discussion data and preprocessing it by removing stop words and punctuation.
2. Implementing inductive content analysis to categorize the data into six types of reflective thinking.
3. Training a Naive Bayes classifier on the categorized data to classify new data.
4. Applying the trained model to large scale unlabeled online discussion data.
5. Using ontology to provide a deeper classification of topics in the data beyond the six reflective thinking categories. This allows extraction of additional knowledge from the classified text data.
This document summarizes a project between IBM and Cycorp to build a prototype question answering system called PIQUANT that integrates information retrieval, natural language processing, and knowledge representation. The system will explore how to best combine these technologies by balancing knowledge stored in structured databases, unstructured text, and a large common-sense knowledge base. It will also develop strategies for locating answers from different knowledge sources and handling different question types. The goal is to begin exploring how to build intelligent question answering systems that can understand the meaning behind text, not just keywords.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
This document provides an overview and requirements for the Stat project, an open source machine learning framework for text analysis. It describes the background, motivation, scope, and stakeholders of the project. Key requirements for the framework include being simplified, reusable, and providing built-in capabilities to naturally support text representation and processing tasks.
Group X analyzed data using computer software. They discussed several types of software for analyzing qualitative data, including those for coding text, developing theories, and building conceptual networks. The functions to look for include coding, memoing, searching, and displaying data. There is no single best software; the researcher must consider their data, approach, and needs. The document provided examples of research articles that used different software like MS Word, NVivo, and Qualrus to analyze qualitative data.
Data analysis – using computers for presentationNoonapau
The document discusses using computer software for data analysis. It provides examples of different types of software including word processors, code-and-retrieve programs, and conceptual network builders. It emphasizes that the researcher should choose software based on their methodology and the type and amount of data, rather than which software is considered "best." The document also summarizes several research articles that used different software programs like MS Word, NVivo, and Qualrus to analyze qualitative data.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Co-Extracting Opinions from Online ReviewsEditor IJCATR
Exclusion of opinion targets and words from online reviews is an important and challenging task in opinion mining. The
opinion mining is the use of natural language processing, text analysis and computational process to identify and recover the subjective
information in source materials. This paper propose a Supervised word alignment model, which identifying the opinion relation. Rather
than this paper focused on topical relation, in which to extract the relevant information or features only from a particular online reviews.
It is based on feature extraction algorithm to identify the potential features. Finally the items are ranked based on the frequency of
positive and negative reviews. Compared to previous methods, our model captures opinion relation and feature extraction more precisely.
One of the most advantages that our model obtain better precision because of supervised alignment model. In addition, an opinion
relation graph is used to refer the relationship between opinion targets and opinion words.
NORMAN, ELTON_BUS7380-8-6 2
NORMAN, ELTON_BUS7380-8-6 1
Analyze Qualitative Data
BUS-7380 Assignment # 6
Elton Norman
Dr. Vicki Lindsay
9 November 2019
Greetings Elton,
Using the same research design that you selected for the Week 5 assignment, you were to take 2-4 pages and consider the type of data collected to create procedures for a comprehensive analysis. Clearly define your approach to: (a) organizing data; (b) coding and thematic development; (c) triangulation; and (d) using software applications.
***************
The feedback process consisted of a four-part summary (four-parts listed below), a few short, location-specific balloon-comments found within the margins of the text, and the highlighting of grammar, punctuation, or APA styling errors found within the text. Make sure that you view your document with the track changes (review toolbar) set to ALL MARKUP to be able to see all the comments.
The summary is split into four parts. These four parts consist of grammar/punctuation, conformity with APA style citations, conformity with APA style references, and content. The order of the parts listed does not intend to emphasize the importance of the parts as the content is always the most important part of the assignment. Therefore, it is listed in the end because normal memory concentrates on what was heard / read last.
What was found:
Grammar/ Punctuation
There were a few grammar or punctuation errors found within. There were problems in spelling, missing punctuation leading to run on sentences, missing punctuation leading to grammatical issues, and the agreement issues between words (i.e., subject/verb agreement and numerical plural numbers without plural noun). Make sure that you read your work prior to submission so that you will not have run on sentences within your work. Pay attention to the word “and” within your work.
APA style citations
The citations present were in APA format. You seem to be missing the additional 3 scholarly sources from your field that were required within this assignment.
APA style references
Not enough information was included within your references to make them correct APA references. You are missing page numbers, volume, issue number, and the digital object identifier for your journal article sources. Your book title should be in italics. The publisher should not. All of the titles should be in sentence case not in title case. This is not a problem with the software program. This is a problem with the keypunch issue. Your program cannot change lowercase letters into uppercase letters in vice versa. You must be it incorrectly for it to properly appear. Many have problems with this thought process. Do not leave it up to the software program to correct keypunch errors.
Content
The same problem that you had an assignment 5 appeared in assignment 6. You are not explaining how these research designs will fit with your research questions or problems statement as you move forward throug ...
The document discusses using computer software to analyze qualitative data, describing different types of analysis software and their functions. It also provides examples of research studies that used various computer-assisted qualitative data analysis software packages like MS Word, NVivo, and NUD*IST to code and analyze interview transcripts, field notes, and other qualitative data sources. The document emphasizes that the choice of software depends on the researcher's methodology, data types and amount, and analysis approach.
Nowadays much is written about how to manage projects, but too little on what really happens in project
actuality. Project Actuality came out in the Rethinking Project Management (RPM) agenda in 2006 and it
aims at understanding what really happens at project context. To be able to understand project actuality
phenomenon, we first need to get a better comprehension on its definition and discover how to observe it
and analyse it. This paper presents the results of the systematic review conducted to collect evidence on
Project Actuality. The research focused on four search engines, in publications from 1994 to 2013. Among
others, the study concludes that project actuality has been analysed by several methods and techniques,
mostly on large organization and public sectors, in Northern Europe. The most common definitions,
techniques, and tips were identified as well as the intent of transforming the results in knowledge.
please just write the bulk of the paper with in text citations and.docxrandymartin91030
please just write the bulk of the paper with in text citations and a work cited page as well don’t worry about title page and header and footer I will edit that upon completion.
To access articles in the Library for this class and others, please refer to the instructions on the Syllabus and in Case 1.
For the session long project, choose one area within the health issue below as your research topic. You will focus on the same topic for your SLP throughout the session.
Traumatic brain injury
Before you begin, read the instructions and expectations carefully -- this is not a typical report-style assignment.
Narrow down the topic to a certain part of the population (i.e. an age group, gender, a certain race or ethnicity, or a particular geographic area). It will help to do some research before choosing your focus, so you can see what literature will be available to use throughout the session. Look at the SLP in Modules 2 - 5 so you can plan ahead as approporiate.
Use credible professional sources such as ProQuest or EBSCO articles, or Websites from a university, government, or nonprofit organization to search for information about the issue. Consumer sources such as e-magazines, newspapers, and .com sites are not appropriate.
1. Introduce the topic and write a brief background about the scope of the problem. What is the health effect? How many people does it affect? Is there a treatment or a cure? What kind of research is being conducted about the problem? This part of the paper should be approximately 1 page.
2. Now, based on what you learned about the topic, think about what the gaps in knowledge seem to be. They are often stated in the "conclusions" of research articles. Using that information, do the following:
State a properly phrased health-related research question that you would like to answer if you were a researcher. Review the information in the link provided on the Background Information page so you are clear as to what a research question is. This should not be a paragraph or an explanation, just a research question.
3. Now, formulate a specific hypothesis to investigate that research question. Again, this should not be a paragraph or an explanation, just a properly stated hypothesis. Review the information in the links provided on the Background Information page so you are clear as to what a hypothesis is.
ASSIGNMENT EXPECTATIONS: Please read before completing assignments.
· Copy the actual assignment from this page onto the cover page of your paper (do this for all papers in all courses).
· Assignment should be 2 pages in length (double-spaced).
· Please use major sections corresponding to the major points of the assignment, and where appropriate use sub-sections (with headings).
· Remember to write in a Scientific manner (try to avoid using the first person except when describing a relevant personal experience).
· Quoted material should not exceed 10% of the total paper (since the focus of these assignments is on independent t.
This document describes a study conducted to understand the factors that influence user trust in complex adaptive agents.
The study involved users interacting with the CALO assistant system over a period of time and then being interviewed. Eight major themes that impacted user trust were identified from the interviews: 1) Usability issues frustrated users, 2) Users felt ignored when the system did not incorporate their feedback, 3) Users wanted context-sensitive explanations from the system, 4) Explanations of the system's reasoning and sources were desired, 5) Transparency into the system's operations increased trust, 6) Changes or surprises in the system's behavior undermined trust, 7) Trust was eroded when the system failed to fulfill expectations, 8)
This document summarizes the results of a study into factors that influence user trust in complex adaptive agents. The study involved users interacting with the CALO assistant system and then being interviewed. The interviews identified 8 major themes that impacted trust, including understandability, transparency, and explanation of an agent's reasoning. Providing explanations was found to address most user trust concerns for adaptive assistants. The document concludes that explanation capabilities are key to building user trust in complex systems with learned and changing behaviors.
Professor Dagobert Soergel's talk (2009 CISTA Award Recipient): Task-centric ...kristenlabonte
"The task-centric revolution. Weaving information into workflows." Systems should be centered around tasks, not applications. This talk will present ideas and techniques towards the design of task-centric systems.
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Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
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The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
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The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
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This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
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Recognizing and Organizing Opinions Expressed in the World ...
1. Recognizing and Organizing Opinions
Expressed in the World Press
Janyce Wiebe, Eric Breck, Chris Buckley, Claire Cardie,
Paul Davis, Bruce Fraser, Diane Litman, David Pierce,
Ellen Riloff, Theresa Wilson, David Day, Mark Maybury
Introduction
Tomorrow’s question answering systems will need The activities of the MPQA project were organized
to have the ability to process information about around an end-user task designed to utilize information
beliefs, opinions, and evaluations—the perspective about perspective—the task of clustering responses to
of an agent. Answers to many simple factual yes/no questions based on perspective. In this task, a
questions—even yes/no questions—are affected by questioner may ask a yes/no question (e.g., question (1)
the perspective of the information source. For above). The system operates as follows: first the question
example, a questioner asking question (1) might be is used as a query to retrieve relevant documents; second,
interested to know that, in general, sources in perspective information is identified in the documents;
European and North American governments tend to third, passages from the documents are clustered based on
answer “no” to question (1), while sources in their text and perspective features. These clusters are
African governments tend to answer “yes:” meant to provide an organization of the documents with
(1) Was the 2002 election in Zimbabwe fair? regard to perspective information to help the questioner
Other questions explicitly ask for information understand them.
about perspective. For example, consider question The remainder of the paper covers the following: The
(2): Tasks section discusses the tasks addressed by the MPQA
(2) What was the reaction of the U.S. State project. The Framework section describes a framework
Department to the 2002 election in Zimbabwe? for annotating, learning, and using information about
In this case, information about the perspective of perspective. The Results section reports the results of our
the U.S. State Department must be identified, both preliminary annotation study, machine learning
as expressed directly by U.S. State Department experiments, and clustering experiments. In the
spokespeople, and indirectly by other sources. annotation study, we found that annotators agreed on
This paper reports on an exploratory project about 85% of direct expressions of opinion, about 50% of
investigating multiple perspectives in question indirect expressions of opinion, and achieved up to 80%
answering (MPQA). The project was conducted as kappa agreement on the rhetorical use of perspective.
a summer workshop.1 While we will not present the annotation scheme or
The purposes of this paper are: agreement study in detail, the results demonstrate the
feasibility of annotating information about perspective.
• To motivate the need for information about For machine learning experiments, we trained a very
opinions in support of question answering. simple classifier for direct expressions of opinion, which
• To introduce a framework for annotating, achieved 66.4% F-measure, nearly 10% over a baseline
learning, and using information about opinions. system. While we have not yet attempted to learn indirect
• To demonstrate that information about opinions perspective expressions and other aspects of the an-
can be effectively annotated. notation scheme, we consider this preliminary result to be
• To demonstrate that information about opinions an indication of the feasibility of automatic recognition of
can be effectively learned. perspective information. Finally, we evaluated our initial
• To formulate a methodology for evaluating the implementation of yes/no clustering with perspective. The
contribution of perspective information to results were mixed: for some topics, perspective informa-
question answering style applications. tion helped to cluster “yes” answer passages together
quite effectively, while for other topics, the information
1
Funded by the Northeast Regional Research Center about perspective did not help. The partial success gives
(NRRC) of the Advanced Research and Development Activity us hope that perspective information will be useful in
(ARDA) a U.S. Government entity which sponsors and question answering, but clearly there is a great deal of
promotes research of import to the Intelligence Community work to be done.
which includes but is not limited to the CIA, DIA, NSA, NIMA,
and NRO.
2. Tasks architecture, human annotators produce annotations of
The specific problems addressed by the MPQA perspective information over the training documents.
project are recognizing and organizing expressions These training annotations are used in the learning
of opinions in the world press and other text. The architecture to train system components to automatically
work builds toward the following tasks to support identify perspective information in new documents. These
activities of professional information analysts. components produce annotations of perspective
information used by the application architecture to cluster
• Given a particular topic, event, or issue, find a document passages.
range of opinions being expressed about it in the A number of general design decisions apply to the
world press. annotation database and the MPQA framework as a
• Once opinions have been found, cluster them whole.
and their sources in useful ways. The source of
an opinion or perspective is simply the person or • The annotation database implements “standoff”, rather
group whose opinion or perspective it is. There than “inline” markup. This means that information
are various attributes according to which about the document is stored separately from the
opinions and their sources may be clustered, in- document text. A benefit is that programs only look at
cluding: the information that they need, without being required
− The type of attitude that is expressed. For to handle a large amount of incidental information.
example, the source might be expressing a • Annotation files are considered immutable objects.
positive, negative, or uncertain attitude. This means that programs may read annotation files,
− The basis for the opinion, such as supporting may write new annotation files, but may never append
beliefs, or experiences. to existing annotation files.
− The expressive style of the sentences. The • The execution model of the framework is “offline”
style might be sarcastic and vehement, for rather than “online”. This means that each component
example, or neutral. of the system may be run separately. A benefit is that
• Once systems are developed to automate the modifications to components and updates to the
above tasks, they may be applied to many topics database can be performed without re-building and re-
and documents, to build perspective profiles of running a large system. (Note that the offline model
various groups and sources, and observe how does not preclude the implementation of a single
attitudes change over time. executable script for running “the system” component
by component.)
To support high-level tasks, such as building
perspective profiles over time and recognizing The remainder of this section briefly describes the
trends and significant changes in opinions, we design of the annotation, learning, and application
developed a representation of how opinions are architectures of the MPQA framework.
expressed in language, and developed a manual
annotation scheme using this representation. The Annotation Architecture
annotation scheme is described in more detail The annotation architecture supports the efforts of human
elsewhere. This paper will focus on the overall annotators to indicate expressions of opinion in text docu-
system architecture and the initial experimental ments. The primary goal of the architecture is to provide a
results. convenient environment for annotators to work in.
The MPQA annotation scheme will be described only
Framework briefly here. The main perspective annotations include
As part of the MPQA project, we developed a direct expressions of potential opinions (namely, “speech
framework for annotating, learning, and using events” and “private states” —together referred to,
information about perspective. We view this somewhat obscurely, as “ons”), and indirect expressions
framework as three “architectures” supporting each of opinions (namely, “expressive subjectivity”). Other
of these three activities. The annotation archi- annotations may include the sources and targets of these
tecture supports the annotation of information opinion expressions, the strengths of the opinions, the
about opinions in text documents by human polarity (negative or positive) of the opinions, and, for
annotators. The learning architecture supports the direct opinions, whether the opinion was presented
development of automatic perspective recognition factively or not.
components via machine learning. The application As an example, consider (3):
architecture supports the yes/no opinion clustering
task. (3) “It is [ES heresy]:’ [ON said] Cao. “The
The framework is organized around a database ‘Shouters’ [ON claim] they are [ES bigger than] Jesus.”
of annotations on documents. In the annotation This example contains direct speech events (ons) by Cao
3. and the ‘Shouters’. In addition, there are machine learning originate from the annotation database.
expressions where Cao’s opinions are expressed Instances are represented as annotations, and feature
indirectly (ES), including heresy and bigger than. values are represented as annotations that occur in the
The annotation architecture was implemented context of one of the instances, allowing both instances
using the annotation tool included in the GATE text and features to be associated with portions of the
processing framework (Cunningham et al. 2002). document. The annotation database thus provides a single
The annotation process is preceded by a document tool for managing all the information in the architecture.
preparation phase. Annotators add perspective A feature generator is a program that consumes a docu-
information to the document. When complete, ment and its annotations as input, and produces more
these annotations are transferred to the annotation annotations as output indicating the features detected in
database. the document. An instance generator is a program that
To prepare documents for annotation, the raw consumes a document and its annotations as input, and
text is extracted. Original markup (e.g., SGML produces output corresponding to the instances of some
markup for title, author, source, date, etc.) is machine learning task. For example, to learn to identify
moved to the annotation database. The document is ons (direct expressions of opinion), an instance generator
imported into GATE and tokens, sentences, and might collect all the verb groups of a document as
part-of-speech tags are identified using potential ons, and one of the feature generators might
components included with GATE. A number of annotate spans of quoted text in the document. Both
annotations are automatically added to the instances and feature annotations may depend on other
document. Since each sentence is considered an feature annotations. For example, the potential on
“implicit” speech event of the writer, these annota- generator above depends on parse annotations to indicate
tions are added automatically. By default, they are the existence of the verb groups. The suite of generator
factive, but the annotator may change this value. programs, coupled with the annotation representation, and
When a document is completely annotated, the the database, provides a flexible architecture for
annotations are exported to the annotation database composing training data for learning. Feature generation
by a custom GATE component that we and instance generation are discussed in more detail
implemented. Another custom GATE component is below.
available to verify a few correctness properties of Instance and feature annotations can be compiled
the perspective annotations. For example, the together and converted to a form suitable for use as
checker will warn the annotator if there is an training data. In a preliminary experiment, we used this
opinion associated with a source, but the source is architecture to learn to automatically identify private
not identified within the document. states and speech events (ons). The description and results
Using the annotation architecture, we have of the experiments are reported in the Results section. To
annotated over 100 documents with perspective summarize the results, we trained two classifiers—using
information. Moreover, the results of an agreement naive Bayes and k-nearest neighbor algorithms, both of
study are given in the Results section. The good which exceeded the performance of a heuristic baseline
results of the agreement study demonstrate that it is system. We currently achieve up to 66.4% f-measure for
possible to annotate opinion information. identifying ons.
The remainder of this section describes the features
Learning Architecture currently included in the learning architecture.
The learning architecture supports the development
of components that learn to automatically identify Text Processing The current implementation of the
perspective information in text. The goals of the learning architecture includes a number of text processing
learning architecture are: components.
• GATE tokenization, sentence splitting, part-of-speech
• to facilitate the use of manually annotated tagging. These preprocessing components are executed
documents as training input for the learning together within GATE.
algorithms; • Alembic tokenization, sentence splitting, part-of-
• to facilitate integration of a variety of text speech tagging. MITRE’s Alembic components are an
processing components as producers of features alternate source of token, sentence, and part-of-speech
for the learning algorithms; annotations.
• to facilitate experimentation with various • Stemmers. Stem annotations are available from both
components and features within a flexible, Porters and Abney’s stemmers.
modular framework. • CASS. CASS is a shallow parser that constructs a flat
• to facilitate evaluation of experimental results. syntactic structure for the document, including noun
and verb chunks, prepositional phrases, and clause
Both the instances and features employed in chunks.
4. employs the components trained within the learning
• Phrag. Phrag named entity annotations indicate architecture. However, for the evaluation reported in the
the presence of entities such as persons, Results section, perspective identification is actually
organizations, locations and dates. performed by our heuristic baseline system (described in
the Framework section), since the learning experiments
Feature Processing In addition to text and clustering experiments were occurring
processing feature generators of the sort listed simultaneously.
above, the architecture also facilitates a more For each document relevant to the query, SMART
declarative specification of features, with a selects the best passage. Candidate passages are
corresponding feature generation program to locate determined by a simple static algorithm that targets
and annotate features according to the passages of length about 800 characters, broken on
specification. sentence boundaries. Overlapping passages are used so
The feature specification language, called TFF, that the first passage might be the first 900 characters of a
encodes feature patterns over words. A pattern document (ending at the first sentence break after 800
indicates the length of the feature in words and the characters), and the second candidate passage might start
particular words and part-of-speech tags that may at character 425 and end at character 1300, again
occur. Additionally, the pattern also indicates the containing only complete sentences.
type of the resulting feature annotation. Pattern (4) We implemented a two-phase agglomerative clustering
is an example: approach to group the best passages. Initially, we start off
(4)type=fixed4gram len=4 word1=what with each passage in a cluster by itself and compute the
pos1=pronoun stemmed1=y word2=a pos2=DT similarity of every cluster to every other cluster by
stemmed2=y word3=bunch pos3=noun computing the passage-passage similarity. In the first
stemmed3=y word4=of pos4=IN stemmed4=y phase, we perform a complete-link merging of clusters.
This pattern matches, for example. ‘What a bunch We take the two clusters with highest similarity to each
of nonsense!” other and then merge them. Afterwards we compute the
The following is a current list of TFF feature new similarity between the newly merged cluster, A, and
specifications: each other cluster, B, by defining the cluster similarity to
be the minimum passage-passage similarity between each
• Speech event verbs from Ballmer and passage of A and each passage of B. We then repeat the
Brennenstuhl (Ballmer & Brennenstuhl 1981). process of merging the two clusters with highest
from Levin (Levin 1993), and from Framenet similarity, until that similarity is below some threshold.
(Framenet). Thus, two clusters in phase 1 will be merged only if every
• Psych verbs from Levin (Levin 1993) and from passage in the first cluster has a sufficiently high
Framenet (Framenet ). similarity to every passage in the second cluster This is a
• Potential subjective element words and phrases very strict merging criteria meant to ensure the core
from Wiebe et al. (Wiebe et al. 2002). clusters are very tight.
• Subjective patterns induced via the meta-
bootstrapping process (Thelen & Riloff 2002). Group ON Agreement ES Agreement
1 0.8450 0.5031
Application Architecture 2 0.7391 0.5034
The application architecture supports the 3 0.8448 0.6895
perspective clustering task. The goals for the
application architecture are: Table 1: Interannotator agreement for ons and expressive-
• To establish a framework for exploring what subjective elements
aspects of opinions are likely to be the most
useful for accomplishing opinion tasks that The second phase, invoked after no cluster-cluster
would be of direct interest to analyst users. complete-link similarity is above the threshold, is to
• To establish a framework for evaluating opinion perform an average-link merging of clusters. In this
tasks. phase, the similarity between cluster A and cluster B is
• To conduct an example evaluation to explore defined to be the average of the similarities of the
what obstacles will be faced in a full evaluation. passages in cluster A to those in cluster B. This is a much
looser criteria and is appropriate for merging the tight
The architecture has three stages—document clusters found in phase 1.
retrieval, perspective identification, and passage Clusters are merged in phase 2 until there are only 3
clustering. The document retrieval stage employs result clusters. There is an additional criteria that no
the SMART information retrieval system. In cluster can contain more than 2/3 of the passages. This
principle, the perspective identification stage ensures that the result is not one huge cluster with 2
5. outlier passages forming their own clusters. of agreement among the annotators within a group is far
from random. As it happens, in each of Groups 1 and 2
Results there is one particularly sensitive annotator who identifies
many more expressive-subjective elements than the other
Annotation Experiments two members of his or her group. It turns out that the
The purpose of the interannotator agreement study other two members’ annotations are largely subsets of the
is to validate our annotations by assessing the sensitive annotators’ annotations. This is not necessarily
consistency of human annotation. In pilot surprising, because we did not calibrate the sensitivity of
interannotator agreement experiments, we annotators’ judgments of expressive-subjective elements.
examined agreement for ons and expressive- Indeed, for various applications, it is likely that either
subjective elements. more or less sensitivity may be appropriate. This is a
Three groups of annotators were involved in the fruitful area for further investigation.
study. Groups 1 and 2 each consisted of three In addition to these agreement results, we achieved up to
project members. Group 3 consisted of a project 80% kappa agreement on the only-factive task for ons that
member and a paid annotator. Within Groups 1 and two annotators agreed upon and that had certain only-
2, there was no prior training among annotators, in factive judgments.
that no two of them had annotated the same
documents and then discussed their results. Learning Experiments
However, the annotation instructions had been The purpose of the learning experiments is to determine
presented to them before, and each of them had whether automatic taggers of perspective information can
annotated some documents. The annotators in be trained using the annotated documents. Our initial
Group 3 had trained together before. Each group experiments target automatic tagging of single-word
annotated a set of three or four documents. direct opinion expressions (ons).
Annotators differ from one another concerning For baseline on identification, we use two lists of
the boundaries of the ons and expressive-subjective speech event verbs. If a word’s lemma was found on one
elements they identify. For applications, it is of the word lists, we tag it; other words and word-
probably most important that both annotators see sequences are left unmarked. The two word lists come
an opinion expression within the same text span, from Levin (Levin 1993) and Framenet (Framenet).
and not that their exact boundaries match. In the For learning ons we used the naive Bayes and k-
experiments, we count overlapping ons and nearest-neighbor implementations included with the Weka
overlapping expressive subjective elements as machine learning toolkit (Witten & Frank 1999). Each
matches. word in a training document comprised a training
Suppose a and b are two annotators. For instance. Features included all words within 2 words on
measuring agreement on between a and b, we either side of the target word, the part of speech of the
calculated agr (a||b), defined to be the proportion of target word, the category from the same two word lists
as annotations that were found by b. This measure used in the baseline system. We also used some features
is appropriate considering that two annotators will derived from the CASS (Abney 1996) partial parser—the
not identify the same number of elements. Since categories of the current word’s chunk, of the previous
agr (a||b) is directional, we also calculated agr (b|| chunk, and of the next chunk. For training, we used all the
a) for each pair. The agreement for a group is the data annotated at the time we ran the experiment. The
average of all pairwise agreement scores. training data consisted of 92 annotated documents
Table 1 presents interannotator agreement containing 63,586 potential on instances.
results. The results for annotating ons are Performance is measured using recall, precision, and f--
particularly encouraging given that the team measure. Given sets of entities G and S annotated in the
members did not train among themselves. The gold standard and by the system, respectively, we have
expressive-subjective results are lower. However, recall R = |G∩S|/|G|, precision P = |G∩S|/|S|, and f-
the pattern measure F = 2PR/(P+R).
Algorithm Precision Recall F-measure Table 2 presents the results of the on tagging experiments.
Baseline 69.9 47.7 56.7 Results for naive Bayes and K-NN are averages over 10-
Naïve Bayes 46.7 76.6 58.0 fold cross-validation. We were pleased that by the F-
K-NN 69.6 63.4 66.4 measure statistic, both learning algorithms bested the
Table 2: Performance results for tagging ons
Zimbabwe
6. Cluster Base Opinions
Both Yes No Neither Both Yes No Neither
1 0 3 11 18 1 5 8 15
2 1 1 5 6 1 1 8 8
3 2 2 4 4 0 1 3 6
Kyoto
Cluster Base Opinions
Both Yes No Neither Both Yes No Neither
1 0 0 7 7 0 2 2 8
2 1 3 4 7 2 2 19 8
3 3 2 3 3 2 1 2 1
Table 3: Cluster evaluation
baseline. These results indicate the feasibility of didn’t match other opinions using whatever strategy was
learning perspective information. being used.
All participants agreed that treatment of supporting
Application Experiments evidence was important, but they disagreed on how to
The purpose of the experiments involving the include it. For example, one had a separate sub-
perspective clustering application is to determine clustering just for evidence. Some included evidence as
whether perspective information could be useful in part of an opinion, others did not. Everybody agreed there
applications of interest to an information analyst. needed to be some way of linking evidence to opinion.
The major question of involving outliers was how
Manual Clustering Study A first step in looking could we distinguish random outliers from outliers that
at automatically clustering documents is to would be important to an analyst. People wanted several
examine how humans cluster, and what are the opinions in each of their clusters or sub-clusters, but an
important issues for humans. Six MPQA project analyst will often be much more interested in the
members plus an ex-analyst manually clustered exceptions: in the one agent in a group whose opinion or
opinions from documents related to 3 topics: tone does not match the rest of the group. No general
1. Election in Zimbabwe. solution to the problem of outliers was proposed, though
2. Treatment of prisoners at Camp X-Ray, it was noted that the particular situation with pro/con top-
Guantanamo Bay. level cluster offers the ability to duplicate sub-clusters in
3. President Bush’s alternative to the Kyoto both the pro and con clusters, thus an important exception
Protocol. might appear on the other side as a sub-cluster of size one.
We measured agreement among the four pro/con two-
There were 19-31 documents per topic, with level cluster participants. The overlap between the sets of
multiple opinions per document. Since the purpose “pro” opinions of two participants ranged from 50-80%.
was to explore what humans might do, the The numbers are a bit fuzzy since participants defined
instructions were deliberately vague. opinion boundaries differently. There was very weak
As might be expected given the lack of agreement at the sub-cluster level, even if two participants
instructions, the participant background strongly constructed sub-clustered using the same basis. For
influenced the type of clusters. One project example, even if the sub- clusters are formed using the
member, a linguist, separately clustered every type of agent expressing the opinion, participants differed
sentence according to the perceived purpose of the as to whether the head of a government task force speaks
sentence. This would be useful for information for the government.
extraction to database. The ex-analyst clustered We also measured whether people agreed on the
according to whether immediate threat of violence boundaries of opinion segments. In general, segment
existed. Four people clustered roughly according to boundary agreement was about 60% for those participants
the proposed end-user task format: they separated who treated evidence the same way.
opinions into pro/con top-level clusters, and then Overall, the lesson learned from this exploratory task is
broke those down into sub-clusters. Nobody’s sub- that clustering is demonstrably important and useful, but
clusters or even sub-cluster strategy agreed with everybody does it differently for different reasons. This
anybody else’s. implies that any evaluation of clustering must be relative
Two major issues that came out of the to a very clearly defined task. In addition, gold standard
discussion were the treatment of supporting evaluation of clusters, where a system’s clustering is
evidence and how to handle outlier opinions that compared against a pre-defined “correct” clustering, is
7. going to be very difficult for anything other than a compared independent of opinions, the Yes, No and
simple clustering task. Also, outlier evaluation Neither answers in the answer segments were scattered
must be explicitly addressed for those tasks where pretty randomly throughout the 3 clusters. For the
it is considered important, and it will not be easy. Opinions trial, where automatic detection of opinions was
used to select and compare passages, the distribution of
Clustering Evaluation Our final experiments Yes/No answers among the 3 clusters improved a bit.
evaluate the end-user perspective clustering task. Given the experimental design where clusters are forced
We constructed a new collection of 271,822 to be merged, success occurs if the minority opinions (in
foreign news documents from June, 2001 to May, this case Yes) are clustered together, possibly with some
2002. The vast majority of these documents are majority opinions added on. For this topic, 6 out of the 9
from FBIS, Foreign Broadcast Information Yes opinions (including the Both figures) occur in one
Service, with a very small number (157) of other cluster. So this aspect of the results yielded a minor
documents gathered from the MITRE MITAP improvement.
systems. (These extra documents were part of our The number of passages that contained no answer to
pilot investigation done before settling on FBIS for the topic question remained just as large in the Opinions
the bulk of the collection.) The total size of the trial as in the Base trial. That’s a clear-cut failure of our
collection is about 1.6 GBytes. algorithms to incorporate opinions into the passage
We also constructed a set of 8 topic questions. selection process.
All 8 topic questions are pro/con questions similar
to question (1). We ran these topics using SMART Zimbabwe
with relevance feedback on the full FBIS Cluster Both Yes No
collection. We identified 40-105 related 1 1 1 18
documents per topic (not all relevant to the original 2 0 1 10
topic). 3 0 23 34
For 4 of the 8 topics we manually identified Kyoto
segments in all the related documents that Cluster Both Yes No
answered the pro/con question. There were 1 1 6 19
generally 0 -- 4 answer segments per document, 2 0 2 1
with each segment generally consisting of 1—3 3 0 4 7
sentences. There was an average of 1.1 answer
segments per document. For each answer segment
Table 4: Retrospective cluster evaluation
we store the agent expressing the answer, and the
start and end of the segment.
Different passages were often chosen, but the passages
For each of the four topics in the collection, we
sometimes included opinion indicators that were unrelated
find the single best passage within each related
to the topic. This lack of coherence is a weakness of
document that answers the question. We then
using static passages; this needs to be explored in future
cluster these passages into a small number of
experiments.
clusters (3 was used here) and evaluate using the
The results of the Kyoto topic are given in Table 3. If
manually determined answer segments. The
anything, the results were less successful than the
clustering is good if “like” opinions (either pro or
Zimbabwe topic. Once again, the number of passages
con) occur together, as determined by the answer
without answer segments remained the same as opinion
segments within each clustered passage.
evidence was added. That result is more reasonable for
The above process is performed twice. In the
this topic than for the Zimbabwe topic; most of the
first experiment, the determination of best passage
passages containing neither answer were in documents
and the clustering between passages is dependent
themselves that did not contain either answer (non-
on the terms within the candidate passages only. In
relevant documents). Given the experimental set-up,
the second experiment, we boost the importance of
nothing can be done with those documents. The minority
the candidate passages and their related similarities
answer for this topic (again Yes) became a bit more
if the passage contains an automatically determined
spread out among the 3 clusters instead of less spread out.
on using the simple word list based heuristics
So this experimental result indicates a failure for our
described in the Framework section. We would
opinion algorithms for this topic also.
hope that the second trial will contain more
The two topics are fairly different when the type of
opinions (as determined by presence of answer
opinions is looked at qualitatively. The Zimbabwe
segments), and that those passages would be better
opinions tend to be rather crisp and short with
clustered into “like” opinions.
substantiating factual evidence. The Kyoto opinions tend
Table 3 gives the results for the Zimbabwe topic.
to be longer and not as strongly stated. Any kind of
For the Base trial, where passages were chosen and
8. clustering or analysis of the Kyoto opinions will be information about perspectives in text, and we have
less successful. Any future work in the area will annotated over 100 documents. Good agreement results
need to ensure that enough topics of varying indicate that opinion annotation is a tractable task, and
difficulty are included. suggest future directions for improving the annotations.
Using the annotated documents as training data, we
Retrospective Evaluation Was the poor trained a classifier to recognize single-word ons. The
performance of the sample simple evaluation task success of this classifier indicates that corpus-based
due to the difficulty in finding opinions, or to the learning of perspective information is a feasible endeavor.
clustering of these opinions? Suppose we could We designed an end-user yes/no clustering application
find opinion passages perfectly. Would our that facilitates evaluation of the utility of perspective
algorithms then be able to cluster them well? information in question answering. In preliminary
These questions suggest a simple retrospective clustering experiments, we found that opinion information
evaluation: Take all passages containing the topic sometimes produces better clusters. More importantly,
answers themselves (giving us perfect knowledge we verified that our evaluation methodology detects
about relevant opinions). Cluster these passages success and failure in our application.
using the same algorithms as previously. All of the work reported here is ongoing. Annotation
Table 4 gives the results for the same Zimbabwe of perspective information continues, and further
and Kyoto topic discussed above, except using the agreement studies are planned. We also plan to continue
answer segments as passages. The Zimbabwe experiments in learning to identify perspective in text by
topic gives almost perfect results. Almost all of the adding expressive-subjective and only-factive tagging
Yes answers, 23 out of 26, occur in Cluster 3. tasks. As the experiments proceed we hope to identify
There are a fair number of No answers in that linguistic features that help to classify opinions. Finally,
cluster also, but that’s unavoidable in this we plan to improve the clustering application by using
experimental design that forces clusters together. more training data, improving the automatic tagging of
The Kyoto topic is again a failure. We were not opinions, and improving the clustering algorithms.
able to group the Yes answers into a single cluster. Ultimately, as we begin to understand the role of
There are several important differences in the perspective in question answering, we hope to move on to
type of passages being clustered in this other question answering tasks that incorporate
retrospective experiment as opposed to the original perspective more fully into answers.
simple experiment. For the Zimbabwe topic, the
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