"Research on Opinion Mining and Sentiment Analysis" project, under the Machine Learning course of the Postgraduate Programme of Computer Science Department, AUTh.
The document discusses experimental causal inference and key concepts in experimental design. It defines causal inference as trying to answer causal questions from data, and experimental causal inference as doing so using experiments rather than observations. The basic ideas of experimental design are outlined as maximizing useful variation, eliminating unhelpful variation, and randomizing what cannot be eliminated. Randomization is described as the key to ensuring treatment groups are statistically equivalent. Some open issues discussed include types of randomization, choice of treatment levels, and other challenges like multiple variables, blocking, and limitations of randomization.
Tweetfix is a visualization platform, developed for the Fix the Fixing european project, where users can explore the results of crowdsourced data analytics from Social Media on well-known Match Fixing cases.
This document summarizes several methods for estimating causal effects from observational data:
1. The back-door criterion provides a method for identifying when causal effects are identifiable based on observable variables. It requires adjusting for a set of variables S that block back-door paths between the treatment X and outcome Y.
2. Estimation methods described include calculating average treatment effects, avoiding estimating high-dimensional marginal distributions using sampling, matching on propensity scores, and using instrumental variables.
3. Propensity score matching involves estimating propensity scores via logistic regression and then matching treated and control units based on their propensity to receive treatment.
4. Instrumental variables estimation uses an instrument I that is associated with treatment X
This document discusses metrics for detecting social media fraud. There are several forms of social media fraud, including identity theft, fake product promotions, and generating fake revenue. Detecting fake accounts and fraud groups is important for social media companies and users to prevent financial losses, damage to company image, and identity theft. Fraud metrics can analyze patterns in the social media network graph using social network analysis techniques. Graph metrics like density, centrality, and connected components can help identify potential fraud behaviors and focus investigations on pivotal nodes in the fraud network. Strongly and weakly connected components are useful for identifying other accounts connected to a known fraudulent user.
This document discusses mining opinion from Twitter data. It begins by providing background on a Singapore government project to develop algorithms for sentiment analysis and discovering online influence. The document then discusses using Twitter data and analyzing retweets and replies to identify influential users. It describes challenges with sentiment analysis, including contextual sentiment and cultural expressions. The document outlines an implementation using support vector machines, SentiWordNet, and key phrases to classify tweets by sentiment. It also discusses using visualization tools and user categorization to analyze sentiment geographically and by user type. A case study of the Verizon iPhone launch is used to test the algorithms.
1) The document discusses several papers related to modeling trust in online networks and communities.
2) Key concepts discussed include TrustRank for identifying reputable web pages, propagation of both trust and distrust in social networks, the EigenTrust algorithm for reputation management in peer-to-peer networks, and attack-resistant trust metrics for public key certification.
3) The document also provides summaries and evaluations of experiments conducted using various real-world datasets to analyze different trust computation models.
Mining Product Opinions and Reviews on the WebFelipe Japm
This document summarizes a presentation on opinion mining and sentiment analysis from user reviews and comments on the web. It discusses the basics of opinion mining, including defining the opinion model and levels of sentiment analysis. It outlines the requirements and design for a system to perform opinion mining, including functional requirements, architecture, and modules for opinion retrieval, composition, feature identification, and sentiment analysis. The design details the implementation of algorithms for identifying opinion words and orientation, as well as aggregating sentiments for features. Evaluation of the system examines effectiveness of feature identification and sentiment classification. Areas for future work are identified to improve handling of complex language patterns and domain-specific contexts.
The document discusses experimental causal inference and key concepts in experimental design. It defines causal inference as trying to answer causal questions from data, and experimental causal inference as doing so using experiments rather than observations. The basic ideas of experimental design are outlined as maximizing useful variation, eliminating unhelpful variation, and randomizing what cannot be eliminated. Randomization is described as the key to ensuring treatment groups are statistically equivalent. Some open issues discussed include types of randomization, choice of treatment levels, and other challenges like multiple variables, blocking, and limitations of randomization.
Tweetfix is a visualization platform, developed for the Fix the Fixing european project, where users can explore the results of crowdsourced data analytics from Social Media on well-known Match Fixing cases.
This document summarizes several methods for estimating causal effects from observational data:
1. The back-door criterion provides a method for identifying when causal effects are identifiable based on observable variables. It requires adjusting for a set of variables S that block back-door paths between the treatment X and outcome Y.
2. Estimation methods described include calculating average treatment effects, avoiding estimating high-dimensional marginal distributions using sampling, matching on propensity scores, and using instrumental variables.
3. Propensity score matching involves estimating propensity scores via logistic regression and then matching treated and control units based on their propensity to receive treatment.
4. Instrumental variables estimation uses an instrument I that is associated with treatment X
This document discusses metrics for detecting social media fraud. There are several forms of social media fraud, including identity theft, fake product promotions, and generating fake revenue. Detecting fake accounts and fraud groups is important for social media companies and users to prevent financial losses, damage to company image, and identity theft. Fraud metrics can analyze patterns in the social media network graph using social network analysis techniques. Graph metrics like density, centrality, and connected components can help identify potential fraud behaviors and focus investigations on pivotal nodes in the fraud network. Strongly and weakly connected components are useful for identifying other accounts connected to a known fraudulent user.
This document discusses mining opinion from Twitter data. It begins by providing background on a Singapore government project to develop algorithms for sentiment analysis and discovering online influence. The document then discusses using Twitter data and analyzing retweets and replies to identify influential users. It describes challenges with sentiment analysis, including contextual sentiment and cultural expressions. The document outlines an implementation using support vector machines, SentiWordNet, and key phrases to classify tweets by sentiment. It also discusses using visualization tools and user categorization to analyze sentiment geographically and by user type. A case study of the Verizon iPhone launch is used to test the algorithms.
1) The document discusses several papers related to modeling trust in online networks and communities.
2) Key concepts discussed include TrustRank for identifying reputable web pages, propagation of both trust and distrust in social networks, the EigenTrust algorithm for reputation management in peer-to-peer networks, and attack-resistant trust metrics for public key certification.
3) The document also provides summaries and evaluations of experiments conducted using various real-world datasets to analyze different trust computation models.
Mining Product Opinions and Reviews on the WebFelipe Japm
This document summarizes a presentation on opinion mining and sentiment analysis from user reviews and comments on the web. It discusses the basics of opinion mining, including defining the opinion model and levels of sentiment analysis. It outlines the requirements and design for a system to perform opinion mining, including functional requirements, architecture, and modules for opinion retrieval, composition, feature identification, and sentiment analysis. The design details the implementation of algorithms for identifying opinion words and orientation, as well as aggregating sentiments for features. Evaluation of the system examines effectiveness of feature identification and sentiment classification. Areas for future work are identified to improve handling of complex language patterns and domain-specific contexts.
This document summarizes a presentation given at the ICCCC 2012 conference in Băile Felix, Romania. It discusses the development of a system to analyze online data related to street protests in Romania from January 2012. The system crawls RSS feeds, identifies topics using LDA, extracts named entities like streets and locations, performs sentiment analysis, and visualizes the results using Google Maps. The summaries aim to provide an overview of the system and highlight its ability to adapt to different crisis situations.
What do you really mean when you tweet? Challenges for opinion mining on soci...Diana Maynard
This talk, given at BRACIS 2013, introduces the topics of opinion mining and social media analytics, in particular looking at the challenges they impose for an NLP system. It investigates the impact of non-standard text in social media, use of sarcasm, swear words, non-words, short sentences, multiple languages and so on, which impede the success of current NLP tools to perform good analysis, and examines tools being developed in some current cutting-edge research projects, including not only text-based research but also multimedia analysis.
This document provides an overview of sentiment analysis and opinion mining. It discusses how sentiment analysis can be used to analyze text and determine the subjective opinions and attitudes expressed. It covers key aspects of sentiment analysis including challenges, common approaches like machine learning classifiers, datasets used, and different levels of analysis like document, sentence, and feature level sentiment classification. The goal of sentiment analysis is to automatically identify positive or negative opinions in text data to understand user sentiment.
This document provides an overview of opinion mining and sentiment analysis. It defines opinion mining as attempting to automatically determine human opinion from natural language text. It discusses some key applications, such as classifying reviews and understanding public opinion. The document also outlines some challenges, such as understanding context and differing domains. It then describes common models for sentiment analysis, including preparing data, analyzing reviews linguistically, and classifying sentiment using techniques like machine learning classifiers.
In recent times, research activities in the areas of Opinion and Sentiment analysis in natural language texts and other media are gaining ground under the umbrella of subjectivity analysis. The reason may be the huge amount of available text data in the Social Web in the forms of news, reviews, blogs, chats and even twitter. Though Sentiment analysis from natural lan-guage text is a multifaceted and multidisciplinary problem, in general, the term “sentiment” is used in reference to the automatic analysis of evaluative text.
Opinion Mining and Sentiment Analysis Issues and Challenges Jaganadh Gopinadhan
This document discusses opinion mining and sentiment analysis. It begins with an introduction to the topic, explaining how people use blogs, forums and social media to share opinions. It then defines sentiment analysis as the automated extraction of subjective content and predicting sentiment from digital text. The document outlines the key components of sentiment analysis, including subjectivity, opinion definition and the components that are analyzed. It also discusses some common approaches to sentiment analysis and some of the main challenges, such as dealing with language, domain specificity, spam and named entity identification.
This document discusses opinion mining for social media. It provides an introduction to opinion mining and sentiment analysis, and discusses some of the challenges involved in performing opinion mining on social media data, including short sentences, incorrect language, and topic divergence. The document then outlines the Arcomem research project, which aims to perform opinion mining on social media to analyze opinions about events over time. It describes the project's entity, topic and opinion extraction workflow and some of the main research directions.
Opinion Mining using Data Mining TechniquesAerofoil Kite
The document discusses opinion mining from Bangla text using data mining techniques. It presents the objectives of developing a software system to analyze opinions from large amounts of Bangla reviews and ease decision making. It covers related works in opinion mining and describes the data mining techniques of Naive Bayes, Maximum Entropy, and Support Vector Machines that will be used. It outlines the procedure for sentiment analysis and opinion mining and discusses future work in developing a Bangla language opinion mining software system with scaling techniques.
Sentiment analysis software uses natural language processing and artificial intelligence to analyze text such as reviews and identify whether the opinions and sentiments expressed are positive or negative. It can help businesses understand customer perceptions of products and brands. While sentiment analysis works reasonably well for classifying simple positive and negative sentiments, it faces challenges in dealing with ambiguity and nuance in human language. The accuracy of sentiment analysis depends on factors such as the complexity of the language analyzed and how finely sentiments are classified.
This document provides an introduction to sentiment analysis. It begins with an overview of sentiment analysis and what it aims to do, which is to automatically extract subjective content like opinions from digital text and classify the sentiment as positive or negative. It then discusses the components of sentiment analysis like subjectivity and sources of subjective text. Different approaches to sentiment analysis are presented like lexicon-based, supervised learning, and unsupervised learning. Challenges in sentiment analysis are also outlined, such as dealing with language, domain, spam, and identifying reliable content. The document concludes with references for further reading.
The document discusses the evolution of the web from Web 1.0 to Web 2.0 and the problems with representing meaning. It introduces semantic web as representing things rather than just documents using semantic annotations in formats like RDFa, microformats and microdata. Linked data allows complex queries across a web of data by embedding semantic annotations and using common schemas like Schema.org. Major companies are now building knowledge graphs to represent structured data from sources on a linked open web.
This document summarizes an analysis of the 2009 Linked Open Data cloud graph. It describes that the graph had 86 vertices and 274 edges, with a diameter of 10 and average path length of 3.916. It also discusses that DBPedia, DBLP, ACM, GeneID, and Geonames had the highest in-degrees and out-degrees, referring to many other datasets. Finally, it concludes that the Linked Open Data provides useful data for reuse through a flexible graph structure compared to traditional databases.
This document discusses incremental clustering techniques for search engines. It introduces Suffix Tree Clustering (STC), which clusters documents incrementally based on common phrases identified using a suffix tree data structure. STC processes documents one by one, updating the suffix tree and base clusters. It aims to provide fast, relevant, and browsable clustering of search results while documents are still being retrieved. Experimental results showed STC can cluster documents in linear time incrementally as opposed to traditional offline clustering of entire datasets.
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
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
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
This document summarizes a presentation given at the ICCCC 2012 conference in Băile Felix, Romania. It discusses the development of a system to analyze online data related to street protests in Romania from January 2012. The system crawls RSS feeds, identifies topics using LDA, extracts named entities like streets and locations, performs sentiment analysis, and visualizes the results using Google Maps. The summaries aim to provide an overview of the system and highlight its ability to adapt to different crisis situations.
What do you really mean when you tweet? Challenges for opinion mining on soci...Diana Maynard
This talk, given at BRACIS 2013, introduces the topics of opinion mining and social media analytics, in particular looking at the challenges they impose for an NLP system. It investigates the impact of non-standard text in social media, use of sarcasm, swear words, non-words, short sentences, multiple languages and so on, which impede the success of current NLP tools to perform good analysis, and examines tools being developed in some current cutting-edge research projects, including not only text-based research but also multimedia analysis.
This document provides an overview of sentiment analysis and opinion mining. It discusses how sentiment analysis can be used to analyze text and determine the subjective opinions and attitudes expressed. It covers key aspects of sentiment analysis including challenges, common approaches like machine learning classifiers, datasets used, and different levels of analysis like document, sentence, and feature level sentiment classification. The goal of sentiment analysis is to automatically identify positive or negative opinions in text data to understand user sentiment.
This document provides an overview of opinion mining and sentiment analysis. It defines opinion mining as attempting to automatically determine human opinion from natural language text. It discusses some key applications, such as classifying reviews and understanding public opinion. The document also outlines some challenges, such as understanding context and differing domains. It then describes common models for sentiment analysis, including preparing data, analyzing reviews linguistically, and classifying sentiment using techniques like machine learning classifiers.
In recent times, research activities in the areas of Opinion and Sentiment analysis in natural language texts and other media are gaining ground under the umbrella of subjectivity analysis. The reason may be the huge amount of available text data in the Social Web in the forms of news, reviews, blogs, chats and even twitter. Though Sentiment analysis from natural lan-guage text is a multifaceted and multidisciplinary problem, in general, the term “sentiment” is used in reference to the automatic analysis of evaluative text.
Opinion Mining and Sentiment Analysis Issues and Challenges Jaganadh Gopinadhan
This document discusses opinion mining and sentiment analysis. It begins with an introduction to the topic, explaining how people use blogs, forums and social media to share opinions. It then defines sentiment analysis as the automated extraction of subjective content and predicting sentiment from digital text. The document outlines the key components of sentiment analysis, including subjectivity, opinion definition and the components that are analyzed. It also discusses some common approaches to sentiment analysis and some of the main challenges, such as dealing with language, domain specificity, spam and named entity identification.
This document discusses opinion mining for social media. It provides an introduction to opinion mining and sentiment analysis, and discusses some of the challenges involved in performing opinion mining on social media data, including short sentences, incorrect language, and topic divergence. The document then outlines the Arcomem research project, which aims to perform opinion mining on social media to analyze opinions about events over time. It describes the project's entity, topic and opinion extraction workflow and some of the main research directions.
Opinion Mining using Data Mining TechniquesAerofoil Kite
The document discusses opinion mining from Bangla text using data mining techniques. It presents the objectives of developing a software system to analyze opinions from large amounts of Bangla reviews and ease decision making. It covers related works in opinion mining and describes the data mining techniques of Naive Bayes, Maximum Entropy, and Support Vector Machines that will be used. It outlines the procedure for sentiment analysis and opinion mining and discusses future work in developing a Bangla language opinion mining software system with scaling techniques.
Sentiment analysis software uses natural language processing and artificial intelligence to analyze text such as reviews and identify whether the opinions and sentiments expressed are positive or negative. It can help businesses understand customer perceptions of products and brands. While sentiment analysis works reasonably well for classifying simple positive and negative sentiments, it faces challenges in dealing with ambiguity and nuance in human language. The accuracy of sentiment analysis depends on factors such as the complexity of the language analyzed and how finely sentiments are classified.
This document provides an introduction to sentiment analysis. It begins with an overview of sentiment analysis and what it aims to do, which is to automatically extract subjective content like opinions from digital text and classify the sentiment as positive or negative. It then discusses the components of sentiment analysis like subjectivity and sources of subjective text. Different approaches to sentiment analysis are presented like lexicon-based, supervised learning, and unsupervised learning. Challenges in sentiment analysis are also outlined, such as dealing with language, domain, spam, and identifying reliable content. The document concludes with references for further reading.
The document discusses the evolution of the web from Web 1.0 to Web 2.0 and the problems with representing meaning. It introduces semantic web as representing things rather than just documents using semantic annotations in formats like RDFa, microformats and microdata. Linked data allows complex queries across a web of data by embedding semantic annotations and using common schemas like Schema.org. Major companies are now building knowledge graphs to represent structured data from sources on a linked open web.
This document summarizes an analysis of the 2009 Linked Open Data cloud graph. It describes that the graph had 86 vertices and 274 edges, with a diameter of 10 and average path length of 3.916. It also discusses that DBPedia, DBLP, ACM, GeneID, and Geonames had the highest in-degrees and out-degrees, referring to many other datasets. Finally, it concludes that the Linked Open Data provides useful data for reuse through a flexible graph structure compared to traditional databases.
This document discusses incremental clustering techniques for search engines. It introduces Suffix Tree Clustering (STC), which clusters documents incrementally based on common phrases identified using a suffix tree data structure. STC processes documents one by one, updating the suffix tree and base clusters. It aims to provide fast, relevant, and browsable clustering of search results while documents are still being retrieved. Experimental results showed STC can cluster documents in linear time incrementally as opposed to traditional offline clustering of entire datasets.
SentiTweet is a sentiment analysis tool for identifying the sentiment of the tweets as positive, negative and neutral.SentiTweet comes to rescue to find the sentiment of a single tweet or a set of tweets. Not only that it also enables you to find out the sentiment of the entire tweet or specific phrases of the tweet.
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
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
8. «The new OnePlus smartphone, OnePlus X, costs
almost 250 dollars, which is half the price other, equal in
characteristics phones cost. Well, I know I would not buy
this one, how can a smartphone have 3Gb of Ram and
yet still be so cheap? It just doesn’t seem right. I’d much
rather buy the slightly more expensive Moto G from
Motorola, at least I know that I’ll pay a bit more but it
will be worth it.»
Opinion Holder
Opinion
Entity / Object
Aspect / Feature