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Table of contents
Contents
List of Figures
List of Tables
1 Introduction
Sentiment Analysis
2 Definitions and terminology
2.1 Opinions, sentiments, and friends
2.2 Summing up
3 Opinion mining and sentiment analysis tasks
3.1 Subjectivity analysis and opinion detection
3.2 Polarity classification
3.3 Opinion holder and target identification
3.4 Opinion summarization
3.5 Irony identification
3.6 Opinion spam identification
4 Polarity classification in detail
4.1 Problem definition
4.2 Issues
4.3 Data
4.4 Evaluation
5 Approaches to polarity classification
5.1 Lexicon based approaches
5.2 Statistical based approaches
Automation and Adaptivity
6 Automatic lexicon construction from microblogs
6.1 Microblogging
6.2 Corpus collection and analysis
6.3 Lexicon construction from Twitter
6.4 Polarity classification
6.5 Conclusions
7 Beyond the bag-of-words model: using dependency graphs
7.1 Motivation
7.2 Related work
7.3 D-grams
7.4 Experiments
7.5 Conclusion
8 Improving weighting schemes for polarity classification
8.1 Data
8.2 Our method
8.3 Experiments and results
8.4 Should a sentiment analysis system be objective?
Applications
9 Disambiguating sentiment ambiguous adjectives in Chinese
9.1 SemEval 2010 task description
9.2 Our approach to sentiment disambiguation
9.3 Experiments and results
9.4 Conclusion
10 Polarity classification of Russian products reviews
10.1 ROMIP 2011 task description
10.2 Our approach to polarity classification
10.3 Experiments and results
10.4 Conclusions
11 Emotion detection in suicide notes
11.1 I2B2 2011 task description
11.2 Related textual analysis of suicide notes
11.3 Our approach to emotion detection
11.4 Experiments and results
11.5 Conclusion
Summary
12 Conclusion
13 Future work
14 Authors’ publications
14.1 International Journals
14.2 Domestic Journals
14.3 International conferences
14.4 Domestic conferences
14.5 Book chapters
14.6 International workshops
14.7 Talks
Bibliography
