In recent years, Twitter Sentiment Analysis (TSA) has become a hot research topic. The target of
this task is to analyse the sentiment polarity of the tweets. There are a lot of machine learning
methods specifically developed to solve TSA problems, such as fully supervised method,
distantly supervised method and combined method of these two. Considering the specialty of
tweets that a limitation of 140 characters, emoticons have important effects on TSA. In this
paper, we compare three emoticon pre-processing methods: emotion deletion (emoDel),
emoticons 2-valued translation (emo2label) and emoticon explanation (emo2explanation).
Then, we propose a method based on emoticon-weight lexicon, and conduct experiments based
on Naive Bayes classifier, to validate the crucial role emoticons play on guiding emotion
tendency in a tweet. Experiments on real data sets demonstrate that emoticons are vital to TSA.
This document provides an overview of sentiment analysis on Twitter. It discusses how sentiment analysis can be used to determine sentiment behind texts and social media updates. The document outlines the methodology used, including data collection from Twitter, preprocessing, feature extraction, and using classifiers like Naive Bayes to predict sentiment. It also discusses applications of sentiment analysis and future areas of improvement, such as using different algorithms and temporal analysis. The goal is to more accurately analyze human sentiment from social media data.
The document presents a study that analyzes sentiment on Twitter using various classification algorithms. It compares the performance of Naive Bayes, Bayes Net, Discriminative Multinomial Naive Bayes, Sequential Minimal Optimization, Hyperpipes, and Random Forest algorithms on a Twitter sentiment dataset. The study finds that Discriminative Multinomial Naive Bayes and Sequential Minimal Optimization algorithms have the best performance with overall F-scores of 0.769 and 0.75, respectively. The study aims to determine the most accurate and efficient algorithms for Twitter sentiment classification.
This document discusses Twitter sentiment analysis using a lexicon-based approach. It covers tokenization, stop word removal, stemming, lemmatization, and using a lexicon to calculate sentiment polarity. Naive Bayes algorithms are described for classification, with applications including real-time prediction, multi-class prediction, text classification, and recommendation systems. Tools mentioned include Anaconda and Spider.
This presentation educates you about Sentimental Analysis, What is sentiment analysis used for?, Challenges of sentiment analysis, How is sentiment analysis done? and Sentiment analysis algorithms.
For more topics stay tuned with Learnbay.
Business Statistics Communicating with Numbers 3rd Edition Jaggia Test BankCleoLeblanc
Full download : https://alibabadownload.com/product/business-statistics-communicating-with-numbers-3rd-edition-jaggia-test-bank/ , Business Statistics Communicating with Numbers 3rd Edition Jaggia Test Bank , Business Statistics Communicating with Numbers,Jaggia,3rd Edition,Test Bank
This document outlines the course "Data Mining & Probabilistic Reasoning" taught at HPI Potsdam in winter 2013/14. It discusses topics like data mining, probabilistic reasoning, machine learning algorithms, and applications. The course covers basics of probability, evaluation measures, classification, regression, clustering, graphical models and more. Example applications discussed include part-of-speech tagging, email classification, recommendation systems, and community detection in social networks.
In this paper we have calculated the polarity of people on two current affairs. For this, the tweets posted by twitter handler network friends and friends of friends on current affairs are extracted from twitter. Following this, two bags of words are created. One is positive bag of words and other is negative bag of words. The objective aspect based polarity evaluation is implemented. The polarity of tweets have illustrated and interpreted with application to understand current digital word in terms of its aspect, perspective by calculating polarity with parameter influencing thoughts, concepts, ideas and aspects of people expressing in digital world
This document provides an overview of sentiment analysis on Twitter. It discusses how sentiment analysis can be used to determine sentiment behind texts and social media updates. The document outlines the methodology used, including data collection from Twitter, preprocessing, feature extraction, and using classifiers like Naive Bayes to predict sentiment. It also discusses applications of sentiment analysis and future areas of improvement, such as using different algorithms and temporal analysis. The goal is to more accurately analyze human sentiment from social media data.
The document presents a study that analyzes sentiment on Twitter using various classification algorithms. It compares the performance of Naive Bayes, Bayes Net, Discriminative Multinomial Naive Bayes, Sequential Minimal Optimization, Hyperpipes, and Random Forest algorithms on a Twitter sentiment dataset. The study finds that Discriminative Multinomial Naive Bayes and Sequential Minimal Optimization algorithms have the best performance with overall F-scores of 0.769 and 0.75, respectively. The study aims to determine the most accurate and efficient algorithms for Twitter sentiment classification.
This document discusses Twitter sentiment analysis using a lexicon-based approach. It covers tokenization, stop word removal, stemming, lemmatization, and using a lexicon to calculate sentiment polarity. Naive Bayes algorithms are described for classification, with applications including real-time prediction, multi-class prediction, text classification, and recommendation systems. Tools mentioned include Anaconda and Spider.
This presentation educates you about Sentimental Analysis, What is sentiment analysis used for?, Challenges of sentiment analysis, How is sentiment analysis done? and Sentiment analysis algorithms.
For more topics stay tuned with Learnbay.
Business Statistics Communicating with Numbers 3rd Edition Jaggia Test BankCleoLeblanc
Full download : https://alibabadownload.com/product/business-statistics-communicating-with-numbers-3rd-edition-jaggia-test-bank/ , Business Statistics Communicating with Numbers 3rd Edition Jaggia Test Bank , Business Statistics Communicating with Numbers,Jaggia,3rd Edition,Test Bank
This document outlines the course "Data Mining & Probabilistic Reasoning" taught at HPI Potsdam in winter 2013/14. It discusses topics like data mining, probabilistic reasoning, machine learning algorithms, and applications. The course covers basics of probability, evaluation measures, classification, regression, clustering, graphical models and more. Example applications discussed include part-of-speech tagging, email classification, recommendation systems, and community detection in social networks.
In this paper we have calculated the polarity of people on two current affairs. For this, the tweets posted by twitter handler network friends and friends of friends on current affairs are extracted from twitter. Following this, two bags of words are created. One is positive bag of words and other is negative bag of words. The objective aspect based polarity evaluation is implemented. The polarity of tweets have illustrated and interpreted with application to understand current digital word in terms of its aspect, perspective by calculating polarity with parameter influencing thoughts, concepts, ideas and aspects of people expressing in digital world
video link => http://youtu.be/D9PBX8FmtpQ
Tweets Classifier which categorises tweets into these 6 categories:
Business
Politics
Music
Health
Sports
Technology
Svm and maximum entropy model for sentiment analysis of tweetsS M Raju
This document summarizes a student project on sentiment analysis of tweets about Apple using two classification algorithms: Support Vector Machine (SVM) and Maximum Entropy. The project collected tweet data, preprocessed it by removing duplicates and correcting errors, and manually labeled the tweets as positive, negative or neutral. The algorithms were tested on this labeled tweet data and evaluated based on accuracy, precision, recall and F-measure. SVM performed better for sentiment classification of tweets. Future work could explore using tweets in other languages and combining SVM kernel subclasses.
Module 9: Natural Language Processing Part 2Sara Hooker
This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Learning graphical models in high dimensional settings
Apr 04, 2017 - Apr 07, 2017
ICMS, 15 South College Street
Probabilistic Relational Models (PRMs) extend Bayesian networks to work with relational databases rather than propositional data. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. We will present in this talk our works about:
- hybrid structure learning methods, with our adaptation of the Max-Min Hill Climbing (MMHC) algorithm proposed for Bayesian networks.
- exact and anytime structure learning methods, with a preliminary work.
This document summarizes an experiment using neural networks for sentiment analysis of tweets. It explores different preprocessing techniques including unigrams, bigrams, stemming, lemmatization and part-of-speech tagging when creating feature vectors from tweets to train multilayer perceptron classifiers. The techniques are evaluated on a labeled tweet corpus, with stemming achieving the best results for 3-level sentiment classification and part-of-speech tagging the best for 5-level. While results are poor, the document suggests ways to improve performance including more complex preprocessing, models and hyperparameter tuning.
This document discusses algorithm-independent machine learning techniques. It introduces concepts like bias and variance, which can quantify how well a learning algorithm matches a problem without depending on a specific algorithm. Methods like cross-validation, bootstrapping, and resampling can be used with different algorithms. While no algorithm is inherently superior, such techniques provide guidance on algorithm use and help integrate multiple classifiers.
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
This document provides an introduction to a machine learning course being taught at Uppsala University. It outlines the schedule, reading list, assignments, and examination. The course covers topics like decision trees, linear models, ensemble methods, text mining, and unsupervised learning. It discusses the differences between supervised and unsupervised learning, as well as classification, regression, and other machine learning techniques. The goal is to introduce students to commonly used methods in natural language processing.
This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.
IRJET - Twitter Sentiment Analysis using Machine LearningIRJET Journal
This document summarizes a research paper on Twitter sentiment analysis using machine learning. It describes extracting tweets on a topic, cleaning the data, extracting features, building a logistic regression model to classify tweets as positive, negative or neutral sentiment, and validating the model. The goal is to analyze public sentiment from Twitter data, which has applications in marketing, product feedback, and other areas.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning course will cover theoretical and practical machine learning concepts. It will include 4 homework assignments and programming in Matlab. Lectures will be supplemented by student-submitted class notes in LaTeX. Topics will include learning approaches like storage and retrieval, rule learning, and flexible model estimation, as well as applications in areas like control, medical diagnosis, and web search. A final exam format has not been determined yet.
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.
These slides cover the final defense presentation for my Doctorate degree. Th...Eric Brown
These slides cover the final defense presentation for my Doctorate degree. The topic: Analysis of Twitter Messages for Sentiment and Insight for use in Stock Market Decision Making.
Twitter Sentiment & Investing - modeling stock price movements with twitter s...Eric Brown
This document discusses research into using Twitter sentiment analysis to predict stock market movements. The researcher collected over 470,000 tweets and used a naïve Bayesian classifier to determine sentiment. Initial analysis found some correlation between sentiment, tweet volume, and the S&P 500 ETF price and volume. Linear regression models were able to predict price direction over 50% of the time when including sentiment, tweet volume, and volatility indexes. Further complex time series models may improve predictions. Overall, the research found Twitter sentiment analysis shows some potential for predicting stock price movements.
Lecture 2: Preliminaries (Understanding and Preprocessing data)Marina Santini
This document provides an overview of key concepts in machine learning and data preprocessing. It discusses raw data and feature representation, including concepts, instances, attributes, and features. It also includes digressions on statistics such as measures of central tendency, normalization, and data visualization techniques. The document is a lecture on preliminaries for machine learning and language technology.
This document presents a lab seminar on semi-supervised learning. It begins with background on semi-supervised learning and examples of applications. It then discusses common semi-supervised learning methods like EM with generative models, co-training, transductive SVMs, and graph-based methods. Next, it covers assumptions of semi-supervised learning, noting the utility of unlabeled data depends on problem structure matching model assumptions. Finally, it proposes future work on multi-edge graph-based semi-supervised learning.
The document proposes a method to recommend users on Q&A sites who are most likely to correctly answer questions. It involves:
1) Classifying questions into tags using logistic regression and SVM models trained on historical data.
2) Calculating a weighted score for each user based on past answer performance for each tag.
3) Recommending top users for tags identified in step 1 as most likely to answer new questions correctly. Experimental results showed this approach worked better for common tags with more training data, while rare tags remained inaccurate to classify. Future work is needed to improve recommendations and user experience.
Basic Evaluation of Antennas Used in Microwave Imaging for Breast Cancer Dete...csandit
Microwave imaging is one of the most promising techniques in diagnosis and screening of
breast cancer and in the medical field that currently under development. It is nonionizing,
noninvasive, sensitive to tumors, specific to cancers, and low-cost. Microwave measurements
can be carried out either in frequency domain or in time domain. In order to develop a
clinically viable medical imaging system, it is important to understand the characteristics of the
microwave antenna. In this paper we investigate some antenna characteristics and discuss
limitations of existing and proposed systems.
EVALUATION AND STUDY OF SOFTWARE DEGRADATION IN THE EVOLUTION OF SIX VERSIONS...csandit
This document discusses evaluating software degradation through six versions of the open-source software JHotDraw. Data was collected dynamically from each version using AspectJ to derive two metrics: entropy and software maturity index. Entropy measures complexity and was expected to decrease as the software evolved from adding new features. The software maturity index was derived from documentation and aimed to find if entropy decreases most when maturity is highest, implying a relationship between the two. Six versions of JHotDraw released over five years were tested to investigate changes in these metrics and degradation as the software evolved through new versions.
video link => http://youtu.be/D9PBX8FmtpQ
Tweets Classifier which categorises tweets into these 6 categories:
Business
Politics
Music
Health
Sports
Technology
Svm and maximum entropy model for sentiment analysis of tweetsS M Raju
This document summarizes a student project on sentiment analysis of tweets about Apple using two classification algorithms: Support Vector Machine (SVM) and Maximum Entropy. The project collected tweet data, preprocessed it by removing duplicates and correcting errors, and manually labeled the tweets as positive, negative or neutral. The algorithms were tested on this labeled tweet data and evaluated based on accuracy, precision, recall and F-measure. SVM performed better for sentiment classification of tweets. Future work could explore using tweets in other languages and combining SVM kernel subclasses.
Module 9: Natural Language Processing Part 2Sara Hooker
This document provides an overview of natural language processing techniques for gathering and analyzing text data, including web scraping, topic modeling, and clustering. It discusses gathering text data through APIs or web scraping using tools like Beautiful Soup. It also covers representing text numerically using bag-of-words and TF-IDF, visualizing documents in multi-dimensional spaces based on word frequencies, and using k-means clustering to group similar documents together based on cosine or Euclidean distances between their vectors. The document uses examples of Netflix movie descriptions to illustrate these NLP techniques.
Lecture 2 Basic Concepts in Machine Learning for Language TechnologyMarina Santini
Definition of Machine Learning
Type of Machine Learning:
Classification
Regression
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Supervised Learning:
Supervised Classification
Training set
Hypothesis class
Empirical error
Margin
Noise
Inductive bias
Generalization
Model assessment
Cross-Validation
Classification in NLP
Types of Classification
Learning graphical models in high dimensional settings
Apr 04, 2017 - Apr 07, 2017
ICMS, 15 South College Street
Probabilistic Relational Models (PRMs) extend Bayesian networks to work with relational databases rather than propositional data. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. We will present in this talk our works about:
- hybrid structure learning methods, with our adaptation of the Max-Min Hill Climbing (MMHC) algorithm proposed for Bayesian networks.
- exact and anytime structure learning methods, with a preliminary work.
This document summarizes an experiment using neural networks for sentiment analysis of tweets. It explores different preprocessing techniques including unigrams, bigrams, stemming, lemmatization and part-of-speech tagging when creating feature vectors from tweets to train multilayer perceptron classifiers. The techniques are evaluated on a labeled tweet corpus, with stemming achieving the best results for 3-level sentiment classification and part-of-speech tagging the best for 5-level. While results are poor, the document suggests ways to improve performance including more complex preprocessing, models and hyperparameter tuning.
This document discusses algorithm-independent machine learning techniques. It introduces concepts like bias and variance, which can quantify how well a learning algorithm matches a problem without depending on a specific algorithm. Methods like cross-validation, bootstrapping, and resampling can be used with different algorithms. While no algorithm is inherently superior, such techniques provide guidance on algorithm use and help integrate multiple classifiers.
Lecture 01: Machine Learning for Language Technology - IntroductionMarina Santini
This document provides an introduction to a machine learning course being taught at Uppsala University. It outlines the schedule, reading list, assignments, and examination. The course covers topics like decision trees, linear models, ensemble methods, text mining, and unsupervised learning. It discusses the differences between supervised and unsupervised learning, as well as classification, regression, and other machine learning techniques. The goal is to introduce students to commonly used methods in natural language processing.
This document provides a summary of a 4-part training program on using PASW Statistics 17 (SPSS 17) software to perform descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. The agenda covers topics like frequency analysis, correlations, t-tests, ANOVA, importing/exporting data, and more. The goal is to help users answer research questions and test hypotheses using techniques in PASW Statistics.
IRJET - Twitter Sentiment Analysis using Machine LearningIRJET Journal
This document summarizes a research paper on Twitter sentiment analysis using machine learning. It describes extracting tweets on a topic, cleaning the data, extracting features, building a logistic regression model to classify tweets as positive, negative or neutral sentiment, and validating the model. The goal is to analyze public sentiment from Twitter data, which has applications in marketing, product feedback, and other areas.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning course will cover theoretical and practical machine learning concepts. It will include 4 homework assignments and programming in Matlab. Lectures will be supplemented by student-submitted class notes in LaTeX. Topics will include learning approaches like storage and retrieval, rule learning, and flexible model estimation, as well as applications in areas like control, medical diagnosis, and web search. A final exam format has not been determined yet.
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.
These slides cover the final defense presentation for my Doctorate degree. Th...Eric Brown
These slides cover the final defense presentation for my Doctorate degree. The topic: Analysis of Twitter Messages for Sentiment and Insight for use in Stock Market Decision Making.
Twitter Sentiment & Investing - modeling stock price movements with twitter s...Eric Brown
This document discusses research into using Twitter sentiment analysis to predict stock market movements. The researcher collected over 470,000 tweets and used a naïve Bayesian classifier to determine sentiment. Initial analysis found some correlation between sentiment, tweet volume, and the S&P 500 ETF price and volume. Linear regression models were able to predict price direction over 50% of the time when including sentiment, tweet volume, and volatility indexes. Further complex time series models may improve predictions. Overall, the research found Twitter sentiment analysis shows some potential for predicting stock price movements.
Lecture 2: Preliminaries (Understanding and Preprocessing data)Marina Santini
This document provides an overview of key concepts in machine learning and data preprocessing. It discusses raw data and feature representation, including concepts, instances, attributes, and features. It also includes digressions on statistics such as measures of central tendency, normalization, and data visualization techniques. The document is a lecture on preliminaries for machine learning and language technology.
This document presents a lab seminar on semi-supervised learning. It begins with background on semi-supervised learning and examples of applications. It then discusses common semi-supervised learning methods like EM with generative models, co-training, transductive SVMs, and graph-based methods. Next, it covers assumptions of semi-supervised learning, noting the utility of unlabeled data depends on problem structure matching model assumptions. Finally, it proposes future work on multi-edge graph-based semi-supervised learning.
The document proposes a method to recommend users on Q&A sites who are most likely to correctly answer questions. It involves:
1) Classifying questions into tags using logistic regression and SVM models trained on historical data.
2) Calculating a weighted score for each user based on past answer performance for each tag.
3) Recommending top users for tags identified in step 1 as most likely to answer new questions correctly. Experimental results showed this approach worked better for common tags with more training data, while rare tags remained inaccurate to classify. Future work is needed to improve recommendations and user experience.
Basic Evaluation of Antennas Used in Microwave Imaging for Breast Cancer Dete...csandit
Microwave imaging is one of the most promising techniques in diagnosis and screening of
breast cancer and in the medical field that currently under development. It is nonionizing,
noninvasive, sensitive to tumors, specific to cancers, and low-cost. Microwave measurements
can be carried out either in frequency domain or in time domain. In order to develop a
clinically viable medical imaging system, it is important to understand the characteristics of the
microwave antenna. In this paper we investigate some antenna characteristics and discuss
limitations of existing and proposed systems.
EVALUATION AND STUDY OF SOFTWARE DEGRADATION IN THE EVOLUTION OF SIX VERSIONS...csandit
This document discusses evaluating software degradation through six versions of the open-source software JHotDraw. Data was collected dynamically from each version using AspectJ to derive two metrics: entropy and software maturity index. Entropy measures complexity and was expected to decrease as the software evolved from adding new features. The software maturity index was derived from documentation and aimed to find if entropy decreases most when maturity is highest, implying a relationship between the two. Six versions of JHotDraw released over five years were tested to investigate changes in these metrics and degradation as the software evolved through new versions.
A LITERATURE REVIEW ON SEMANTIC WEB – UNDERSTANDING THE PIONEERS’ PERSPECTIVEcsandit
There are various definitions, view and explanations about Semantic Web, its usage and its underlying architecture. However, the various flavours of explanations seem to have swayed way off-topic to the real purpose of Semantic Web. In this paper, we try to review the literature of Semantic Web based on the original views of the pioneers of Semantic Web which includes, Sir Tim Berners-Lee, Dean Allemang, Ora Lassila and James Hendler. Understanding the vision of the pioneers of any technology is cornerstone to the development. We have broken down Semantic Web into two approaches which allows us to reason with why Semantic Web is not mainstream.
Understanding the Impact of the Social Media on Digital Marketing and E-comme...Rohit Pawar
This document discusses the impact of social media on digital marketing and e-commerce. It explains that social media enables interaction without physical meetings and provides opportunities for customer engagement and information access. Social media influences user behavior by tracking browsing history, which is important for e-businesses to understand. Social media also allows businesses to gain wider reach through evolution of social commerce, which helps develop trust in the e-commerce sector. Product reviews on social media can strengthen e-commerce, while negative reviews are promptly addressed to build loyalty and retention.
Key Management Scheme for Secure Group Communication in WSN with Multiple Gr...csandit
Security is one of the inherent challenges in the area of Wireless Sensor Network (WSN). At
present, majority of the security protocols involve massive iterations and complex steps of
encryptions thereby giving rise to degradation of quality of service. Many WSN applications are
based on secure group communication. In this paper, we have proposed a scheme for secure
group key management with simultaneous multiple groups. The scheme uses a key-based
approach for managing the groups and we show that membership change events can be
handled with less storage, communication and computation cost. The scheme also offers
authentication to the messages communicated within and among the groups.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
A FLOATING POINT DIVISION UNIT BASED ON TAYLOR-SERIES EXPANSION ALGORITHM AND...csandit
Floating point division, even though being an infrequent operation in the traditional sense, is
indis-pensable when it comes to a range of non-traditional applications such as K-Means
Clustering and QR Decomposition just to name a few. In such applications, hardware support
for floating point division would boost the performance of the entire system. In this paper, we
present a novel architecture for a floating point division unit based on the Taylor-series
expansion algorithm. We show that the Iterative Logarithmic Multiplier is very well suited to be
used as a part of this architecture. We propose an implementation of the powering unit that can
calculate an odd power and an even power of a number simultaneously, meanwhile having little
hardware overhead when compared to the Iterative Logarithmic Multiplier.
CORRELATION OF EIGENVECTOR CENTRALITY TO OTHER CENTRALITY MEASURES: RANDOM, S...csandit
In this paper, we thoroughly investigate correlations of eigenvector centrality to five centrality
measures, including degree centrality, betweenness centrality, clustering coefficient centrality,
closeness centrality, and farness centrality, of various types of network (random network, smallworld
network, and real-world network). For each network, we compute those six centrality
measures, from which the correlation coefficient is determined. Our analysis suggests that the
degree centrality and the eigenvector centrality are highly correlated, regardless of the type of
network. Furthermore, the eigenvector centrality also highly correlates to betweenness on
random and real-world networks. However, it is inconsistent on small-world network, probably
owing to its power-law distribution. Finally, it is also revealed that eigenvector centrality is
distinct from clustering coefficient centrality, closeness centrality and farness centrality in all
tested occasions. The findings in this paper could lead us to further correlation analysis on
multiple centrality measures in the near future
Robust Visual Tracking Based on Sparse PCA-L1csandit
Recently, visual tracking based on sparse principle component analysis has drawn much
research attention. As we all know, principle component analysis (PCA) is widely used in data
processing and dimensionality reduction. But PCA is difficult to interpret in practical
application and all those principal components are linear combinations of all variables. In our
paper, a novel visual tracking method based on sparse principal component analysis and L1
tracking is introduced, which we named the method SPCA-L1 tracking. We firstly introduce
trivial templates of L1 tracking method, which are used to describe noise, into PCA appearance
model. Then we use lasso model to achieve sparse coefficients. Then we update the eigenbasis
and mean incrementally to make the method robust when solving different kinds of changes of
the target. Numerous experiments, where the targets undergo large changes in pose, scale and
illumination, demonstrate the effectiveness and robustness of the proposed method.
Este documento proporciona una introducción a varias herramientas de búsqueda y comunicación de Google, incluyendo operadores booleanos para realizar búsquedas avanzadas, la creación de sitios web sin conocimientos de HTML a través de Google Sites, herramientas de comunicación como Chat de Gmail y calendario de Google, y aplicaciones como YouTube, Google Drive y Google+.
Exploring The Dynamic Integration of Heterogeneous Services csandit
The increase need for services to handle a plethora of business needs within the enterprise
landscape has yielded to an increase in the development of heterogeneous services across the
digital world. In today’s digital economy, services are the key components for communication
and collaboration amongst enterprises internally and externally. Since Internet has stimulated
the use of services, different services have been developed for different purposes prompting
those services to be heterogeneous due to incompatibles approaches relied upon at both
conceptual and exploitation phases. The proliferation of developed heterogeneous services in
the digital world therefore comes along with a range of challenges more precisely in the
integration layer. Traditionally, integration is achieved by using gateways, which require
considerable configuration effort. Many approaches and frameworks have been developed by
different researchers to overcome these challenges, but up to date the challenges of integration
heterogeneous services with minimal user-involvement still exist. In this paper, we are exploring
the challenges of heterogeneous services and characteristics thereof with the aim of developing
a seamless approach that will alleviate some of these challenges in near future. It is therefore of
outmost importance to understand the challenges and characteristics of heterogeneous services
before developing a mechanism that could eliminate these challenges.
WIRELESS SENSORS INTEGRATION INTO INTERNET OF THINGS AND THE SECURITY PRIMITIVEScsandit
The common vision of smart systems today, is by and large associated with one single concept,
the internet of things (IoT), where the whole physical infrastructure is linked with intelligent
monitoring and communication technologies through the use of wireless sensors. In such an
intelligent vibrant system, sensors are connected to send useful information and control
instructions via distributed sensor networks. Wireless sensors have an easy deployment and
better flexibility of devices contrary to wired setup. With the rapid technological development of
sensors, wireless sensor networks (WSNs) will become the key technology for IoT and an
invaluable resource for realizing the vision of Internet of things (IoT) paradigm. It is also
important to consider whether the sensors of a WSN should be completely integrated into IoT or
not. New security challenges arise when heterogeneous sensors are integrated into the IoT. Security needs to be considered at a global perspective, not just at a local scale. This paper gives an overview of sensor integration into IoT, some major security challenges and also a
number of security primitives that can be taken to protect their data over the internet.
Several solutions exist for file storage, sharing, and synchronization. Many of them involve a
central server, or a collection of servers, that either store the files, or act as a gateway for them
to be shared. Some systems take a decentralized approach, wherein interconnected users form a
peer-to-peer (P2P) network, and partake in the sharing process: they share the files they
possess with others, and can obtain the files owned by other peers.
In this paper, we survey various technologies, both cloud-based and P2P-based, that users use
to synchronize their files across the network, and discuss their strengths and weaknesses.
Starbucks aims to create an experience, not just sell coffee. They have over 8000 stores globally serving specialty coffee and focus on high-quality beans. The founders started in 1971 and Howard Schultz joined in 1982, later acquiring the company. Starbucks creates its experience through carefully sourced products, specially designed store locations, and an emphasis on employee satisfaction. They have expanded globally while maintaining rigorous standards for coffee selection and store design.
The document provides an overview of Tata Steel, one of India's largest steel companies. It discusses Tata Steel's history, operations in 26 countries, acquisition of Corus Group in 2007, ongoing projects in India and other countries, financial ratios from 2010-2015, comparisons to other steel companies, and potential opportunities and challenges. The conclusion evaluates whether to buy Tata Steel shares at the current price based on the company's plans to raise funds and buy back shares.
The document analyzes the social media presence and website optimization of several makeup brands that are major competitors to Rohit Pawar. It finds that Maybelline has the most Facebook likes and indexed web pages, while Lakme has the most followers across Facebook, Instagram, and Twitter. The document recommends ideal times and days of the week to share content on Facebook, and notes that Rohit Pawar's Twitter followers have diverse interests outside of fashion.
What is Sleeping pralysis and how it effect us ?Harshit Agarwal
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Making use of social media for analyzing the perceptions of the masses over a product, event or a person has
gained momentum in recent times. Out of a wide array of social networks, we chose Twitter for our analysis as
the opinions expressed their, are concise and bear a distinctive polarity. Here, we collect the most recent tweets
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neutral. We do the classification in following manner: collect the tweets using Twitter API; then we process the
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Sentiment classification is an ongoing field and interesting area of research because of its application in various fields collecting review from people about products and social and political events through the web. Currently, Sentiment Analysis concentrates for subjective statements or on subjectivity and overlook objective statements which carry sentiment(s). During the sentiment classification more challenging problem are faced due to the ambiguous sense of words, negation words and intensifier. Due to its importance the correct sense of target word is extracted and determined for which the similarity arise in WordNet Glosses. This paper presents a survey covering the techniques and methods in sentiment analysis and challenges appear in the field.
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Nowadays Sentiment Analysis play an important Role in each field such as Stock market, product reviews, news article, political debates which help us to determining current trend in the market regarding specific product, event, issues. Here we are apply sentiment analysis on microblogging platforms such as twitter, Facebook which is used by different people to express their opinion with respect to different kind of foods in the field of home’schef. This paper explain different methods of text preprocessing and applies them with a naive Bayes classifier in a big data, distributed computing platform with the goal of creating a scalable sentiment analysis solution that can classify text into positive or negative categories. We apply negation handling, word n-grams, stemming, and feature selection to evaluate how different combinations of these pre-processing methods affect performance and efficiency.
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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.
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This document presents a live Twitter sentiment analysis application that performs sentiment analysis on tweets in real-time about a topic entered by the user. It uses the Twitter API to stream tweets, the VADER sentiment analysis library to analyze sentiment, and pyLDAvis for topic modeling. The application cleans tweets by removing emojis, stopwords, punctuations, and lemmatizing words. It then uses VADER to classify sentiment and displays results interactively. PyLDAvis performs topic modeling on tweets to discover topics and display keywords. The application allows users to explore emotions and themes in Twitter conversations about their interests of interest in a dynamic, interactive manner.
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The document presents research on identifying depressive moods on Twitter using a convolutional neural network model. Key points:
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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 discusses techniques for real-time emotion classification of tweets and other social media posts. It describes an architecture for classifying emotions in tweets using machine learning algorithms like naive Bayes classifiers. Feature extraction methods like n-grams and TF-IDF are used to transform text into numerical feature vectors. The document also evaluates methods for generating contextualized lexicons to assess user opinion in social networks, including a polarity-length approach and seeds-based approach. Finally, it covers an explanation system for a technique called ARBITER that forecasts scientific and technical trends by analyzing publications. ARBITER uses ontologies and Bayesian networks to model evidence, hypotheses and probabilistic relationships between variables.
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
Analyzing Sentiment Of Movie Reviews In Bangla By Applying Machine Learning T...Andrew Parish
This document summarizes a research paper that analyzed sentiment of movie reviews written in Bangla using machine learning techniques. The researchers collected a dataset of over 4,000 Bangla movie reviews labeled as positive or negative. Using this dataset, they tested support vector machine and long short-term memory models, achieving 88.9% and 82.42% accuracy respectively. The paper also reviewed other prior work on Bangla sentiment analysis and compared different machine learning methods.
This document discusses using machine learning for sentiment analysis on Twitter data. It defines machine learning and different types of machine learning like supervised and unsupervised learning. It then defines sentiment analysis as identifying subjective information from text and classifying it as positive, negative, or neutral. The document outlines the process of collecting Twitter data, preprocessing it, analyzing sentiment using algorithms like Naive Bayes and decision trees, and presenting the results. It acknowledges challenges like informal language and discusses how the proposed system could provide useful insights for businesses.
This document presents a summary of sentiment analysis techniques for classifying tweets as having positive or negative sentiment. It discusses representing text as bag-of-words vectors and using a Naive Bayes classifier trained on labeled tweets. The techniques covered include preprocessing text, removing stop words, stemming, constructing word n-grams, and building word frequency vectors. The document concludes that a Naive Bayes approach using pre-trained word vectors achieves good performance for Twitter sentiment analysis.
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2. 66 Computer Science & Information Technology (CS & IT)
Twitter is one of the most popular online social networking service today, which allow users to
send and read short messages called tweets. With tweets, people can share with other people what
they are doing and thinking [5]. According to recent statistical data1
, as of March 2016, there
have been more than 310 million monthly active users and 330 million tweets are generated every
day. The most important feature of Twitter is that every tweet is a message up to 140 characters.
It is because of this character limitation that emoticon become very important in tweets, since
emoticon can help people better express their emotion in a short message. However, most of the
researchers have dismissed emoticons as noisy information and delete them in the pre-processing
process. Nevertheless, we will explore the influence of emoticons on SA in this paper.
Very often SA is applied on movies review and news article [3] [4] [6]. Compared with movie
reviews and news articles, tweets have a lot of difference [7]. On the one hand, tweets are shorter
and more ambiguous than movie reviews and news articles because of the limitation of words.
On the other hand, tweets contain much more misspelled words, slang, modal particles and
acronyms because of the casual form. Considering these difference, the traditional SA methods
for movie reviews and news articles are not appropriate for Twitter Sentiment Analysis (TSA)
problem. Actually, many novel SA methods have been specifically developed for TSA, which
include fully supervised method and distantly supervised method. With manually labelled data,
fully supervised methods like Multinomial Naive Bayes (MNB) and support vector machine
(SVM) are more accurate, but labelling data manually is more labour-intensive and time
consuming. With data collected by Twitter API, distantly supervised methods are more efficient
but less accurate. [8] even combined these two methods and developed the emoticon smoothed
language models (ESLAM) for TSA.
In this study, we explore the effects of emoticons on TSA. At first, we compare three emoticon
pre-processing methods: emotion deletion (emoDel), emoticons 2valued translation (emo2label)
and emoticon explanation (emo2explanation). After that, we propose a method based on
emoticon-weight lexicon to explore the influence of emotion on TSA. Experiments on real data
sets demonstrate that emoticons are vital to TSA.
2. RELATED WORK
SA [1] has been a popular research topic over the past decades. Before [2], knowledge-based
method dominated this domain. However, in [2], authors show that machine learning techniques
like naive Bayes, maximum entropy and support vector machine can outperform the knowledge-
based baselines on movie reviews. After that, machine learning based methods have become the
most important methods for SA.
With the rapidly growth of Twitter, more and more researchers started to focus on TSA. Most of
earlier works on TSA are fully supervised methods. In [9] [10], authors use traditional SA
methods on normal text form to solve TSA problems. Authors propose target-independent SA
based on SVM in [11]. In [12], authors present a dynamic artificial neural network to handle
TSA.
Recently, different supervised methods are proposed. Authors in [13] utilize Twitter API to get
training data which contain emoticons like :) and :(. They use these emoticons as noisy labels.
1
https://about.twitter.com/company
3. Computer Science & Information Technology (CS & IT) 67
Tweets with :) are thought to be positive training data and tweets with :( are thought to be
negative training data.
In [8], authors present the ESLAM which combine fully supervised methods and distantly
supervised methods. Although a lot of TSA methods have been presented, few of them explored
the influence of emoticons on TSA, which motivates our work in this paper.
3. EXPLORE EFFECTS OF EMOTICONS
In this section, first we present our basic TSA classifier based on Naive Bayes (NB). Then, we
introduce an emoticon lexicon which contain 50 most commonly used emoticons. After that, we
present 3 emoticon pre-processing methods: emoDeletion, emo2label and emo2explanation.
Finally, we propose a method based on emoticon-weight lexicon and introduce a strategy to
integrate emoticon-weight lexicon method with naive Bayes method.
3.1. Naive Bayes (NB) Model for SA
In this paper, we use a Twitter-aware tokenizer2
combined with a Naïve Bayes model as our basic
classifier. Refer to the Stanford Classifier3
, here is the basic idea for the Naive Bayes:
We assume that:
• n is the number of words appeared in training set T,
• n_cj is the number of feature which belong to class j (cj) in training set T (j can be
positive or negative),
• n_fi is the number of times feature i appeared in training set T,
• n_fi_ci is the number of times feature i appeared in class j.
Then, we use the following equations to compute the probabilities p_cj and p_fi_cj:
ܿ_ =
_ೕାఌ
ା|௦௦௦|×ఌ
(1)
݂__ܿ =
__ೕାఙ
_ା|௦௦௦|×ఙ
(2)
While we have two classes (positive and negative), so |classes| = 2.
In (1) (2), the parameters ɛ and σ are smoothing parameters to avoid assigning zero weight to
unseen feature. In our experiment, we choose ɛ = 10−30
and σ = 1.0 (Laplacian smoothing).
With (1) (2), we can compute negative weight and positive weight of every feature:
ܹ, = log ൬
__ೕ
_ೕ
൰ (3)
2
http://sentiment.christopherpotts.net/code-data/happyfuntokenizing.py
3
http://nlp.stanford.edu/software/classifier.shtml
4. 68 Computer Science & Information Technology (CS & IT)
After get weights of all features, we can compute the weights of sentences according to Naive
Bayes assumption.
Assuming that tweet t consists of n features, then the weights of the tweet t will be:
ܹ_݁ܿ݊݁ݐ݊݁ݏ௧, = ∑ ܹ,
ୀଵ (4)
Finally, we will compute the possibilities of the sentence belonging to negative class and positive
class:
,
, ,
( | )
t neg
t neg t pos
W
W W
e
P t neg
e e
=
+
(5)
,
, ,
( | )
t pos
t neg t pos
W
W W
e
P t pos
e e
=
+
(6)
3.2. Emoticon Lexicon
Our emoticon lexicon is based on a Twitter emoticon analysis4
which collected a large number of
most commonly used emoticons. We choose the top 50 emoticons as our emoticon lexicon.
For every emoticon, we give a polarity value which can be negative or positive, a specific
translation and a weight. This lexicon is showed in Table 1. We will use this emoticon lexicon in
subsequent parts.
Table 1. Emoticon Lexicon
Emoticon Value Translation Weight
:) :D :-) ;) XD :] =) (: ;-) =D =] :-D ^_^ (8 :o) (;=o 8)
;o) (= [: 8D :]
POSITIVE happy 1
:o ;O o: POSITIVE surprise 1
=P :-P ;P =P POSITIVE playful 1
;D ;] POSITIVE wink 1
m/ POSITIVE salute 1
:( D: =( ): ;) :[ ;( =[ NEGATIVE sad -1
=/ :-/ : ;/ :-/ = NEGATIVE annoyed -1
:’( NEGATIVE crying -1
:@ NEGATIVE angry -1
:| NEGATIVE indifferent -1
3.3. Emoticon Pre-processing Methods
EmoDeletion: In this emoticon pre-processing method, we just delete all the emoticons defined
in emoticon lexicon in TABLE 1 from the training data.
4
http://www.datagenetics.com/blog/october52012/index.html
5. Computer Science & Information Technology (CS & IT) 69
Emo2label: This emoticon pre-processing method is pretty simple and straightforward. We give
all the emoticons a 2-valued label: NEGATIVE or POSITIVE. We give a label of NEGATIVE to
those emoticons with negative meanings and give a label of POSITIVE to those emoticons with
positive meanings. This kind of translation is not so close to natural language, but it is more
intuitive and robust because it could avoid some translation errors. For both training data and test
data, when we find any emoticon defined in emoticon lexicon, we replace it with its 2-valued
labels in pre-processing.
Emo2explanation: When two people communicate face to face, they could notice the expression
like “smile” or “frown” made by the other. For example, A is frowning and says to B “I’m fine”.
If C asks B the recent situation of A, B will not ignore A’s expression but translate A’s
expression naturally. B will say: “I saw some days ago. She said she was fine but I noticed she
was frowning. So I think maybe she met some trouble.” Such like that, almost every emoticon
can be described as a verbal word and it is much easier for a computer to recognize a word rather
than an emoticon since most of the features extracted by classifier are words. Because of the
similarity of some emoticons, we organize emoticons into emoticon synonymy sets, which we
define as groups of emoticons with the same translation (see TABLE 1). From both training data
and testing data, when we find any emoticon defined in emoticon lexicon, we replace it with its
translation in pre-processing. For example, a tweet “This movie so cool!! :)” are translated into
“This movie so cool!! happy” after pre-processing.
3.4. Emoticon-Wight Lexicon Model (EWLM) for SA
In polarity classification, we place a text into negative or positive class. Similarly, we use a polar
weight to define an emoticon which is a character sequences. For an emoticon with positive
meaning, we give it the value 1, otherwise, we give it the value -1 [14]. The format of an
emoticon-weight lexicon is (emoticon, weight), for example, (:), 1), (:(, -1).
When classifying a text, we consider both emoticons and verbal cues, and combine the two
factors to get an integrated assessment to the text. The framework is as below [Figure 1]: Firstly,
we load a set of tweets for analysing sentiment. Then, the classifier split it into different tweets.
For each tweet, we check if this tweet contains emoticon.
Figure 1. Framework architecture
6. 70 Computer Science & Information Technology (CS & IT)
We compare each word in the tweet with the emoticon lexicon entries. If there exist emoticons
which match the emoticons in lexicon, we compute the emoticon score of this tweet and combine
this score with words score. Otherwise, we just use the words score which is given by the NB
classifier. When the tweet i contains emoticon, ei = 1, otherwise ei = 0. i.e.
݁ = ቄ
0, ݊ ݁݉݊ܿ݅ݐ ݅݊ ݐ݁݁ݓݐ ݅
1, ݁ݐݏ݅ݔ ݁݉݊ܿ݅ݐ ݅݊ ݐ݁݁ݓݐ ݅
(7)
For every tweet, the NB classifier gives us two probabilities piw(neg) and piw(pos) for classifying
verbal cues. If piw(neg) > piw(pos), the NB classifier places the tweet into negative class.
Otherwise, the tweet is placed into positive class. When ei = 1, the emoticon score of ith
tweet sie
equals the sum of weights of each emoticon. Assuming that the number of emoticons in ith
tweet
is Ni (Ni > 0), and the weight of jth
emoticon is W_emoj, we have:
ܵ = ∑ ܹ_ೕ
ே
ୀଵ (8)
The emoticon-weight lexicon helps us to deal with only emoticons. The NB classifier deals with
verbal cues. Hence, we need a combination strategy to combine EWLM with NB classifier, to get
a final classification result.
As above, sie is the sum of weight of emoticons in tweet i, which is not in the range of (0, 1). We
use the Sigmoid function to convert the range of sie into a new range which is between 0 and 1,
because we need to combine this value with a probability value which is between 0 and 1 given
by the NB classifier. With Sigmoid function, we can compute P_EWLM:
ܲாௐெሺݏ|ݐሻ = ܵ݅݃݉݀݅ሺܵሻ (9)
ܲாௐெሺ݃݁݊|ݐሻ = 1 − ܵ݅݃݉݀݅ሺܵሻ (10)
The sentiment of both emoticons and verbal cues can be computed as a probability of being
negative or positive. We use α as a factor, which decides the importance of the emoticon in a
tweet, to integrate these two probabilities and get the final probabilities. pi(pos) is the probability
of the ith
tweet being positive, and pi(neg) is the probability of the ith tweet being negative. If α ≥
0.5, verbal cues play a more important role. Otherwise, the emoticon occupies a greater
proportion on analysing sentiment.
ܲሺ݊݁݃ሻ =∝× ܲேሺሻ + ሺ1−∝ሻ × ܲாௐெሺ݊݁݃ሻ (11)
ܲሺݏሻ =∝× ܲேሺ௦ሻ + ሺ1−∝ሻ × ܲாௐெሺݏሻ (12)
The classification ci of ith
tweet is defined as a function of its final probabilities pi(neg) and
pi(pos):
ܿ = ൜
,݁ݒ݅ݐ݅ݏ ݂݅ ܲሺݏሻ ≥ ܲሺ݊݁݃ሻ
݊݁݃ܽ,݁ݒ݅ݐ ݂݅ ܲሺݏሻ < ܲሺ݊݁݃ሻ
(13)
7. Computer Science & Information Technology (CS & IT) 71
4. EXPERIMENT DESIGN
4.1. Data Set
We use the publicly available Sanders Corpus5
as our experiment data, which consist of 5513
manually labelled tweets. These tweets involved with four different topics: Apple, Google,
Microsoft, and Twitter. After removing the no English tweets, spam tweets, re-tweets and
duplicate tweets, and setting the classes to be balanced, we get 952 tweets for polarity
classification, including 476 negative tweets and 476 positive tweets. There are 200 tweets which
contain emoticons in the whole data set (which means approximately 21% tweets contain
emoticons).
We take the following measures to pre-process the data:
1. Replace the Twitter usernames which start with @ with USERNAME.
2. Replace urls in tweets with URL.
3. All words are changed to their lower cases. With these pre-processing measures, we can
reduce the influence of meaningless strings and extract more representative features.
4.2. Experiment Setting
We assume that the total number of data, including training data and test data, is X (= 952). For
every experiment, we randomly sample the same amount of tweets (say Y, Y = 16, 32, 64...) for
both negative class and positive class as our training set, and use the rest X − 2Y tweets as our
test set. In order to avoid the experiment contingency, every time we will conduct 60 times
experiments independently and get the average performance, which is more accurate.
4.3. Evaluation
We evaluate the performance of our experiments by the values of accuracy and Macro-level F1-
score. Accuracy is the percentage of correctly predicted data in all test data. The Macro-level F1-
score is the average of the F1-scores of the positive and negative classifiers, where F1-score is the
harmonic mean of precision and recall. F1-score is related with precision and recall calculated by
the simplified formula [14]:
1ܨ =
ଶ×௦ ×ோ
௦ାோ
(14)
5. EXPERIMENT RESULTS
5.1. Effects of emoticon pre-processing methods
We conduct experiments based on NB model to compare with and without emoticon pre-
processing methods and explore the influence of emoticons. In this experiment, we use different
number of training data (i.e. 2Y = 32,64,128,256,512,768). The results are illustrated by Figure 2
with accuracy and Figure 3 with Macro-level F1-score.
5
http://www.sananalytics.com/lab/twitter-sentiment/
8. 72 Computer Science & Information Technology (CS & IT)
Figure 2. Effects of emoticon pre-processing methods measured by Accuracy
Figure 3. Effects of emoticon pre-processing methods measured by Macro level F1 score
From Figure 2 and Figure 3, we can easily see that emo2label has the best performance among
the proposed emoticon pre-processing methods.
5.2. Effects of Emoticon-Weight Lexicon Model
We compare the performance of the NB model with and without EWLM to judge if EWLM can
help the NB model to raise the performance on TSA. In this experiment, we also use different
9. Computer Science & Information Technology (CS & IT) 73
training size to train the classifier and utilizer accuracy and Micro-level F1 score to evaluate the
classifier.
The experiment result is showed in Figure 4 and Figure 5.
Figure 4. Effects of EWLM measured by accuracy
Figure 5. Effects of EWLM measured by Macro-level F1 score
From Figure 4 and Figure 5, it is obvious that EWLM can help the NB model to raise the
performance on TSA, especially when the training size is small. When the training size is big
enough, the data can provide more discriminating information for training the NB classifier, and
the NB classifier could achieve a better performance. In this condition, the improvement brought
by EWLM will become smaller. Anyway, the experiment results imply that the emoticons do
have important information which could help the NB classifier to achieve better performance on
TSA tasks.
10. 74 Computer Science & Information Technology (CS & IT)
5.3. Effects of the Combination Parameter Alpha
Alpha is a significant factor to combine NB model with EWLM. When alpha equals 1, there will
be only NB model to conduct TSA task. When alpha is smaller, the EWLM will play a more
important role in the combined classifier. In this experiment, we try different value of alpha to
check which value of alpha is best. The experiment results can be seen in Figure 6 (training size
equals 128) and Figure 7 (training size equals 512).
Figure 6. Effect of combination factor alpha with 128 training data
Figure 7. Effect of combination factor alpha with 512 training data
11. Computer Science & Information Technology (CS & IT) 75
The experiments result in Figure 6 and Figure 7 clearly show that the combination strategy is
better than the single NB model or single EWLM. Furthermore, we can see that when alpha take
values from 0.1 to 0.3, the classifier can achieve the best performance. Also, in the experiment
with 512 training example, we can notice that when alpha becomes larger, the performance of the
classifier will not be influenced a lot. This is because a large manually labelled training data can
provide enough discriminating information for the TSA classifier.
Our results could clearly indicate that considering emoticon into classifier is a necessary addition
on sentiment analysis and whether a tweet contains emoticon or not, our methods will not weaken
the performance. If no emoticon in data, the performance of our methods is same with NB
classifier.
6. CONCLUSIONS
With the significance of sentiment analysis being recognized and the popularity rate of emoticon
in social network getting higher and higher, the role of emoticon cannot be ignored on polarity
classification. Our key contribution in this paper lies in validating the important role emoticon
plays in conveying overall sentiment of a text in TSA though a series of experiments.
We compare 3 emoticon pre-processing methods and emoticon-weight lexicon method on the
base of Twitter aware tokenizer and NB Model. We propose a combination strategy using factor
alpha to integrate the Emoticon-Weight Lexicon with classifier. The result shows that the usage
of emoticon-weight lexicon model improves the performance of NB model on TSA task. We can
get the conclusion that some emoticons dominate the sentiment of a tweet and conquer the
emotion of verbal cues.
As our results are very promising, we assume several directions for further work. First, we will
look for some authoritative help to improve our emoticon dictionary and set more detailed score
for emoticon weight to show its intensity of emotion. Second, we will study the impact of number
of emoticons in experimental data on our emoticon weight lexicon.
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AUTHORS
Katarzyna Węgrzyn-Wolska received M.Sc. from the Silesian Technical University
of Gliwice (Poland), and a further M.Sc. in Computer Science from the University of
Val Essonne (France), her Ph.D. (2001) in Automatics, Real Time Computing and
Computer Science from the Ecole Superieur des Mines de Paris, France and the
habilitation (H.D.R), to become Full Professor in 2012. She is the Principal Professor
and head of an SITR team at ESIGETEL, France. She is editor-in-chief of the
International Journal on Social Informatics edited in ICST Transactions Series. She is
involved in the organization of several International Conference as well as an expert for the group
Information Society Technologies (IST) active in the European Community. Her main interests are
Information Retrieval, Search Engines, Web Based Support Systems, Web Intelligence and Social
Networks.
13. Computer Science & Information Technology (CS & IT) 77
Lamine Bouguroua: He is a Research Associate Professor at school of Computer
Science and Engineering ESIGETEL (Ecole Supérieure d’Informatique et Génie des
Télécommunications), Villejuif, France. He received the Ph.D. degree in Sciences from
the University of Paris XII, Paris, France, in March 2007. His research interests include
programming languages, software architecture, object-oriented software systems,
program analysis, scheduling, embedded systems, real time systems and fault tolerance.
Today, his activity is concerned with scheduling and fault tolerance in Real-Time
Systems, social network, multi-agent system.