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E-grāmata: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

4.00/5 (22 ratings by Goodreads)
(University of Illinois, Chicago)
  • Formāts: PDF+DRM
  • Izdošanas datums: 04-Jun-2015
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316308462
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Sentiment Analysis: Mining Opinions, Sentiments, and EmotionsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 04-Jun-2015
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316308462
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"Opinion and sentiment and their related concepts such as evaluation, appraisal, attitude, affect, emotion and mood are about our subjective feelings and beliefs. They are central to the human psychology and are key influencers of our behaviors. Our beliefs and perceptions of reality, as well as the choices we make, are to a considerable degree conditioned upon how others see and perceive the world. Due to this reason, our views about the world are very much influenced by those of others, and whenever weneed to make a decision we often seek out others' opinions. This is not only true for individuals but also true for organizations. From an application point of view, we naturally want to mine people's opinions and feelings toward any subject matter of interest, which is the task of sentiment analysis. More precisely, sentiment analysis, which is also called opinion mining, is a field of study that aims to extract opinions and sentiments from natural language text using computational methods"--

"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 thelanguage 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"--

Recenzijas

'As a whole, this book serves as a useful introduction to sentiment analysis along with in-depth discussions of linguistic phenomena related to sentiments, opinions, and emotions. Although many sentiment analysis methods are based on machine learning as in other NLP [ Natural Language Processing] tasks, sentiment analysis is much more than just a classification or regression problem, because the natural language constructs used to express opinions, sentiments, and emotions are highly sophisticated, including sentiment shift, implicated expression, sarcasm, and so on. Liu has described these issues and problems very clearly. Readers will find this book to be inspiring and it will arouse their interests in sentiment analysis.' Jun Zhao, Computational Linguistics

Papildus informācija

This book gives a comprehensive introduction to all the core areas and many emerging themes of sentiment analysis.
Preface xi
Acknowledgments xv
1 Introduction 1(15)
1.1 Sentiment Analysis Applications
4(4)
1.2 Sentiment Analysis Research
8(6)
1.2.1 Different Levels of Analysis
9(1)
1.2.2 Sentiment Lexicon and Its Issues
10(1)
1.2.3 Analyzing Debates and Comments
11(1)
1.2.4 Mining Intentions
12(1)
1.2.5 Opinion Spam Detection and Quality of Reviews
12(2)
1.3 Sentiment Analysis as Mini NLP
14(1)
1.4 My Approach to Writing This Book
14(2)
2 The Problem of Sentiment Analysis 16(31)
2.1 Definition of Opinion
17(12)
2.1.1 Opinion Definition
17(2)
2.1.2 Sentiment Target
19(1)
2.1.3 Sentiment of Opinion
20(2)
2.1.4 Opinion Definition Simplified
22(2)
2.1.5 Reason and Qualifier for Opinion
24(1)
2.1.6 Objective and Tasks of Sentiment Analysis
25(4)
2.2 Definition of Opinion Summary
29(2)
2.3 Affect, Emotion, and Mood
31(8)
2.3.1 Affect, Emotion, and Mood in Psychology
31(5)
2.3.2 Affect, Emotion, and Mood in Sentiment Analysis
36(3)
2.4 Different Types of Opinions
39(6)
2.4.1 Regular and Comparative Opinions
39(1)
2.4.2 Subjective and Fact-Implied Opinions
40(4)
2.4.3 First-Person and Non-First-Person Opinions
44(1)
2.4.4 Meta-Opinions
44(1)
2.5 Author and Reader Standpoint
45(1)
2.6 Summary
45(2)
3 Document Sentiment Classification 47(23)
3.1 Supervised Sentiment Classification
49(8)
3.1.1 Classification Using Machine Learning Algorithms
49(7)
3.1.2 Classification Using a Custom Score Function
56(1)
3.2 Unsupervised Sentiment Classification
57(4)
3.2.1 Classification Using Syntactic Patterns and Web Search
57(2)
3.2.2 Classification Using Sentiment Lexicons
59(2)
3.3 Sentiment Rating Prediction
61(2)
3.4 Cross-Domain Sentiment Classification
63(2)
3.5 Cross-Language Sentiment Classification
65(2)
3.6 Emotion Classification of Documents
67(1)
3.7 Summary
68(2)
4 Sentence Subjectivity and Sentiment Classification 70(20)
4.1 Subjectivity
72(1)
4.2 Sentence Subjectivity Classification
73(3)
4.3 Sentence Sentiment Classification
76(4)
4.3.1 Assumption of Sentence Sentiment Classification
77(1)
4.3.2 Classification Methods
78(2)
4.4 Dealing with Conditional Sentences
80(2)
4.5 Dealing with Sarcastic Sentences
82(2)
4.6 Cross-Language Subjectivity and Sentiment Classification
84(2)
4.7 Using Discourse Information for Sentiment Classification
86(1)
4.8 Emotion Classification of Sentences
87(1)
4.9 Discussion
88(2)
5 Aspect Sentiment Classification 90(47)
5.1 Aspect Sentiment Classification
91(7)
5.1.1 Supervised Learning
92(1)
5.1.2 Lexicon-Based Approach
93(3)
5.1.3 Pros and Cons of the Two Approaches
96(2)
5.2 Rules of Sentiment Composition
98(18)
5.2.1 Sentiment Composition Rules
99(7)
5.2.2 DECREASE and INCREASE Expressions
106(3)
5.2.3 SMALL_OR_LESS and LARGE_OR_MORE Expressions
109(3)
5.2.4 Emotion and Sentiment Intensity
112(1)
5.2.5 Senses of Sentiment Words
112(2)
5.2.6 Survey of Other Approaches
114(2)
5.3 Negation and Sentiment
116(7)
5.3.1 Negation Words
116(3)
5.3.2 Never
119(2)
5.3.3 Some Other Common Sentiment Shifters
121(1)
5.3.4 Shifted or Transferred Negations
122(1)
5.3.5 Scope of Negations
122(1)
5.4 Modality and Sentiment
123(4)
5.5 Coordinating Conjunction But
127(2)
5.6 Sentiment Words in Non-opinion Contexts
129(2)
5.7 Rule Representation
131(2)
5.8 Word Sense Disambiguation and Coreference Resolution
133(2)
5.9 Summary
135(2)
6 Aspect and Entity Extraction 137(52)
6.1 Frequency-Based Aspect Extraction
138(2)
6.2 Exploiting Syntactic Relations
140(9)
6.2.1 Using Opinion and Target Relations
141(6)
6.2.2 Using Part-of and Attribute-of Relations
147(2)
6.3 Using Supervised Learning
149(4)
6.3.1 Hidden Markov Models
150(1)
6.3.2 Conditional Random Fields
151(2)
6.4 Mapping Implicit Aspects
153(4)
6.4.1 Corpus-Based Approach
153(1)
6.4.2 Dictionary-Based Approach
154(3)
6.5 Grouping Aspects into Categories
157(2)
6.6 Exploiting Topic Models
159(20)
6.6.1 Latent Dirichlet Allocation
160(3)
6.6.2 Using Unsupervised Topic Models
163(5)
6.6.3 Using Prior Domain Knowledge in Modeling
168(3)
6.6.4 Lifelong Topic Models: Learn as Humans Do
171(3)
6.6.5 Using Phrases as Topical Terms
174(5)
6.7 Entity Extraction and Resolution
179(7)
6.7.1 Problem of Entity Extraction and Resolution
179(4)
6.7.2 Entity Extraction
183(1)
6.7.3 Entity Linking
184(1)
6.7.4 Entity Search and Linking
185(1)
6.8 Opinion Holder and Time Extraction
186(1)
6.9 Summary
187(2)
7 Sentiment Lexicon Generation 189(13)
7.1 Dictionary-Based Approach
190(3)
7.2 Corpus-Based Approach
193(6)
7.2.1 Identifying Sentiment Words from a Corpus
194(1)
7.2.2 Dealing with Context-Dependent Sentiment Words
195(2)
7.2.3 Lexicon Adaptation
197(1)
7.2.4 Some Other Related Work
198(1)
7.3 Desirable and Undesirable Facts
199(1)
7.4 Summary
200(2)
8 Analysis of Comparative Opinions 202(16)
8.1 Problem Definition
202(4)
8.2 Identify Comparative Sentences
206(1)
8.3 Identifying the Preferred Entity Set
207(2)
8.4 Special Types of Comparison
209(6)
8.4.1 Nonstandard Comparison
209(2)
8.4.2 Cross-Type Comparison
211(1)
8.4.3 Single-Entity Comparison
212(2)
8.4.4 Sentences Involving Compare and Comparison
214(1)
8.5 Entity and Aspect Extraction
215(1)
8.6 Summary
216(2)
9 Opinion Summarization and Search 218(13)
9.1 Aspect-Based Opinion Summarization
219(2)
9.2 Enhancements to Aspect-Based Summary
221(3)
9.3 Contrastive View Summarization
224(1)
9.4 Traditional Summarization
225(1)
9.5 Summarization of Comparative Opinions
225(1)
9.6 Opinion Search
226(1)
9.7 Existing Opinion Retrieval Techniques
227(2)
9.8 Summary
229(2)
10 Analysis of Debates and Comments 231(19)
10.1 Recognizing Stances in Debates
232(3)
10.2 Modeling Debates/Discussions
235(11)
10.2.1 JTE Model
236(4)
10.2.2 JTE-R Model: Encoding Reply Relations
240(3)
10.2.3 JTE-P Model: Encoding Pair Structures
243(2)
10.2.4 Analysis of Tolerance in Online Discussions
245(1)
10.3 Modeling Comments
246(2)
10.4 Summary
248(2)
11 Mining Intentions 250(9)
11.1 Problem of Intention Mining
250(4)
11.2 Intention Classification
254(2)
11.3 Fine-Grained Mining of Intentions
256(2)
11.4 Summary
258(1)
12 Detecting Fake or Deceptive Opinions 259(44)
12.1 Different Types of Spam
262(7)
12.1.1 Harmful Fake Reviews
262(1)
12.1.2 Types of Spammers and Spamming
263(2)
12.1.3 Types of Data, Features, and Detection
265(2)
12.1.4 Fake Reviews versus Conventional Lies
267(2)
12.2 Supervised Fake Review Detection
269(3)
12.3 Supervised Yelp Data Experiment
272(3)
12.3.1 Supervised Learning Using Linguistic Features
273(1)
12.3.2 Supervised Learning Using Bahavioral Features
274(1)
12.4 Automated Discovery of Abnormal Patterns
275(7)
12.4.1 Class Association Rules
276(1)
12.4.2 Unexpectedness of One-Condition Rules
277(3)
12.4.3 Unexpectedness of Two-Condition Rules
280(2)
12.5 Model-Based Behavioral Analysis
282(3)
12.5.1 Spam Detection Based on Atypical Behaviors
282(1)
12.5.2 Spain Detection Using Review Graph
283(1)
12.5.3 Spam Detection Using Bayesian Models
284(1)
12.6 Group Spam Detection
285(6)
12.6.1 Group Behavior Features
288(2)
12.6.2 Individual Member Behavior Features
290(1)
12.7 Identifying Reviewers with Multiple Userids
291(7)
12.7.1 Learning in a Similarity Space
292(1)
12.7.2 Training Data Preparation
293(1)
12.7.3 d-Features and s-Features
294(1)
12.7.4 Identifying Userids of the Same Author
295(3)
12.8 Exploiting Burstiness in Reviews
298(2)
12.9 Some Future Research Directions
300(1)
12.10 Summary
301(2)
13 Quality of Reviews 303(6)
13.1 Quality Prediction as a Regression Problem
303(2)
13.2 Other Methods
305(1)
13.3 Some New Frontiers
306(1)
13.4 Summary
307(2)
14 Conclusions 309(6)
Appendix 315(12)
Bibliography 327(36)
Index 363
Bing Liu is a Professor of Computer Science at the University of Illinois. His current research interests include sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals, and his research has been cited on the front page of the New York Times. He is also the author of two books: Sentiment Analysis and Opinion Mining (2012) and Web Data Mining: Exploring Hyperlinks, Contents and Usage Data (1st edition, 2007; 2nd edition, 2011). He currently serves as the chair of ACM SIGKDD and is an IEEE Fellow.
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