Sentiment Analysis Mining Opinions Sentiments and Emotions 1st edition by Bing Liu – Ebook PDF Instant Download/Delivery: 1107017890, 978-1107017894
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ISBN 10: 1107017890
ISBN 13: 978-1107017894
Author: Bing Liu
Sentiment analysis is the computational study of people’s opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.
Sentiment Analysis Mining Opinions Sentiments and Emotions 1st Table of contents:
Acknowledgments
1 Introduction
1.1 Sentiment Analysis Applications
1.2 Sentiment Analysis Research
1.2.1 Different Levels of Analysis
1.2.2 Sentiment Lexicon and Its Issues
1.2.3 Analyzing Debates and Comments
1.2.4 Mining Intentions
1.2.5 Opinion Spam Detection and Quality of Reviews
1.3 Sentiment Analysis as Mini NLP
1.4 My Approach to Writing This Book
2 The Problem of Sentiment Analysis
2.1 Definition of Opinion
2.1.1 Opinion Definition
2.1.2 Sentiment Target
2.1.3 Sentiment of Opinion
2.1.4 Opinion Definition Simplified
2.1.5 Reason and Qualifier for Opinion
2.1.6 Objective and Tasks of Sentiment Analysis
2.2 Definition of Opinion Summary
2.3 Affect, Emotion, and Mood
2.3.1 Affect, Emotion, and Mood in Psychology
2.3.2 Affect, Emotion, and Mood in Sentiment Analysis
2.4 Different Types of Opinions
2.4.1 Regular and Comparative Opinions
2.4.2 Subjective and Fact-Implied Opinions
2.4.3 First-Person and Non-First-Person Opinions
2.4.4 Meta-Opinions
2.5 Author and Reader Standpoint
2.6 Summary
3 Document Sentiment Classification
3.1 Supervised Sentiment Classification
3.1.1 Classification Using Machine Learning Algorithms
3.1.2 Classification Using a Custom Score Function
3.2 Unsupervised Sentiment Classification
3.2.1 Classification Using Syntactic Patterns and Web Search
3.2.2 Classification Using Sentiment Lexicons
3.3 Sentiment Rating Prediction
3.4 Cross-Domain Sentiment Classification
3.5 Cross-Language Sentiment Classification
3.6 Emotion Classification of Documents
3.7 Summary
4 Sentence Subjectivity and Sentiment Classification
4.1 Subjectivity
4.2 Sentence Subjectivity Classification
4.3 Sentence Sentiment Classification
4.3.1 Assumption of Sentence Sentiment Classification
4.3.2 Classification Methods
4.4 Dealing with Conditional Sentences
4.5 Dealing with Sarcastic Sentences
4.6 Cross-Language Subjectivity and Sentiment Classification
4.7 Using Discourse Information for Sentiment Classification
4.8 Emotion Classification of Sentences
4.9 Discussion
5 Aspect Sentiment Classification
5.1 Aspect Sentiment Classification
5.1.1 Supervised Learning
5.1.2 Lexicon-Based Approach
5.1.3 Pros and Cons of the Two Approaches
5.2 Rules of Sentiment Composition
5.2.1 Sentiment Composition Rules
5.2.2 DECREASE and INCREASE Expressions
5.2.3 SMALL_OR_LESS and LARGE_OR_MORE Expressions
5.2.4 Emotion and Sentiment Intensity
5.2.5 Senses of Sentiment Words
5.2.6 Survey of Other Approaches
5.3 Negation and Sentiment
5.3.1 Negation Words
5.3.2 Never
5.3.3 Some Other Common Sentiment Shifters
5.3.4 Shifted or Transferred Negations
5.3.5 Scope of Negations
5.4 Modality and Sentiment
5.5 Coordinating Conjunction But
5.6 Sentiment Words in Non-opinion Contexts
5.7 Rule Representation
5.8 Word Sense Disambiguation and Coreference Resolution
5.9 Summary
6 Aspect and Entity Extraction
6.1 Frequency-Based Aspect Extraction
6.2 Exploiting Syntactic Relations
6.2.1 Using Opinion and Target Relations
6.2.2 Using Part-of and Attribute-of Relations
6.3 Using Supervised Learning
6.3.1 Hidden Markov Models
6.3.2 Conditional Random Fields
6.4 Mapping Implicit Aspects
6.4.1 Corpus-Based Approach
6.4.2 Dictionary-Based Approach
6.5 Grouping Aspects into Categories
6.6 Exploiting Topic Models
6.6.1 Latent Dirichlet Allocation
6.6.2 Using Unsupervised Topic Models
6.6.3 Using Prior Domain Knowledge in Modeling
6.6.4 Lifelong Topic Models: Learn as Humans Do
6.6.5 Using Phrases as Topical Terms
6.7 Entity Extraction and Resolution
6.7.1 Problem of Entity Extraction and Resolution
6.7.2 Entity Extraction
6.7.3 Entity Linking
6.7.4 Entity Search and Linking
6.8 Opinion Holder and Time Extraction
6.9 Summary
7 Sentiment Lexicon Generation
7.1 Dictionary-Based Approach
7.2 Corpus-Based Approach
7.2.1 Identifying Sentiment Words from a Corpus
7.2.2 Dealing with Context-Dependent Sentiment Words
7.2.3 Lexicon Adaptation
7.2.4 Some Other Related Work
7.3 Desirable and Undesirable Facts
7.4 Summary
8 Analysis of Comparative Opinions
8.1 Problem Definition
8.2 Identify Comparative Sentences
8.3 Identifying the Preferred Entity Set
8.4 Special Types of Comparison
8.4.1 Nonstandard Comparison
8.4.2 Cross-Type Comparison
8.4.3 Single-Entity Comparison
8.4.4 Sentences Involving Compare or Comparison
8.5 Entity and Aspect Extraction
8.6 Summary
9 Opinion Summarization and Search
9.1 Aspect-Based Opinion Summarization
9.2 Enhancements to Aspect-Based Summary
9.3 Contrastive View Summarization
9.4 Traditional Summarization
9.5 Summarization of Comparative Opinions
9.6 Opinion Search
9.7 Existing Opinion Retrieval Techniques
9.8 Summary
10 Analysis of Debates and Comments
10.1 Recognizing Stances in Debates
10.2 Modeling Debates/Discussions
10.2.1 JTE Model
10.2.2 JTE-R Model: Encoding Reply Relations
10.2.3 JTE-P Model: Encoding Pair Structures
10.2.4 Analysis of Tolerance in Online Discussions
10.3 Modeling Comments
10.4 Summary
11 Mining Intentions
11.1 Problem of Intention Mining
11.2 Intention Classification
11.3 Fine-Grained Mining of Intentions
11.4 Summary
12 Detecting Fake or Deceptive Opinions
12.1 Different Types of Spam
12.1.1 Harmful Fake Reviews
12.1.2 Types of Spammers and Spamming
12.1.3 Types of Data, Features, and Detection
12.1.4 Fake Reviews versus Conventional Lies
12.2 Supervised Fake Review Detection
12.3 Supervised Yelp Data Experiment
12.3.1 Supervised Learning Using Linguistic Features
12.3.2 Supervised Learning Using Behavioral Features
12.4 Automated Discovery of Abnormal Patterns
12.4.1 Class Association Rules
12.4.2 Unexpectedness of One-Condition Rules
12.4.3 Unexpectedness of Two-Condition Rules
12.5 Model-Based Behavioral Analysis
12.5.1 Spam Detection Based on Atypical Behaviors
12.5.2 Spam Detection Using Review Graph
12.5.3 Spam Detection Using Bayesian Models
12.6 Group Spam Detection
12.6.1 Group Behavior Features
12.6.2 Individual Member Behavior Features
12.7 Identifying Reviewers with Multiple Userids
12.7.1 Learning in a Similarity Space
12.7.2 Training Data Preparation
12.7.3 d-Features and s-Features
12.7.4 Identifying Userids of the Same Author
12.8 Exploiting Burstiness in Reviews
12.9 Some Future Research Directions
12.10 Summary
13 Quality of Reviews
13.1 Quality Prediction as a Regression Problem
13.2 Other Methods
13.3 Some New Frontiers
13.4 Summary
14 Conclusions
Appendix
Bibliography
Index
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