Sentiment analysis: mining opinions, sentiments and emotions
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York
Cambridge University Press
2015
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVI, 367 Seiten Illustrationen |
| ISBN: | 9781107017894 |
Internformat
MARC
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| 008 | 150119s2015 a||| |||| 00||| eng d | ||
| 020 | |a 9781107017894 |9 978-1-107-01789-4 | ||
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| 245 | 1 | 0 | |a Sentiment analysis |b mining opinions, sentiments and emotions |c Bing Liu, University of Illinois at Chicago |
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| 300 | |a XVI, 367 Seiten |b Illustrationen | ||
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-027715231 | ||
Datensatz im Suchindex
| _version_ | 1804152840782872576 |
|---|---|
| adam_text | Contents
Preface page
xi
Acknowledgments
xv
1
Introduction
1
1.1
Sentiment Analysis Applications
4
1.2
Sentiment Analysis Research
8
1.2.1
Different Levels of Analysis
9
1.2.2
Sentiment Lexicon and Its Issues
10
1.2.3
Analyzing Debates and Comments
11
1.2.4
Mining Intentions
12
1.2.5
Opinion Spam Detection and Quality of Reviews
12
1.3
Sentiment Analysis as Mini NLP
14
1.4
My Approach to Writing This Book
14
2
The Problem of Sentiment Analysis
16
2.1
Definition of Opinion
17
2.1.1
Opinion Definition
17
2.1.2
Sentiment Target
19
2.1.3
Sentiment of Opinion
20
2.1.4
Opinion Definition Simplified
22
2.1.5
Reason and Qualifier for Opinion
24
2.1.6
Objective and Tasks of Sentiment Analysis
25
2.2
Definition of Opmion Summary
29
2.3
Affect, Emotion, and
Vlood
31
2.3.1
Affect, Emotion, and Mood in Psychology
3]
2.3.2
Affect, Emotion, and Mood in Sentiment Analysis
36
2.4
Different Types of Opinions
39
2.4.1
Regular and Comparative Opinions
39
2.4.2
Subjective and Fact-Implied Opinions
40
2.4.3
First-Person and Non-First-Person Opinions
44
vi
Contents
2.4.4 Meta-Opinions 44
2.5
Author and Reader Standpoint
45
2.6
Summary
45
3
Document Sentiment Classification
47
3.1
Supervised Sentiment Classification
49
3.1.1
Classification Using Machine Learning Algorithms
49
3.1.2
Classification Using a Custom Score Function
56
3.2
Unsupervised Sentiment Classification
57
3.2.1
Classification Using Syntactic Patterns and Web Search
57
3.2.2
Classification Using Sentiment Lexicons
59
3.3
Sentiment Rating Prediction
61
3.4
Cross-Domain Sentiment Classification
63
3.5
Cross-Language Sentiment Classification
65
3.6
Emotion Classification of Documents
67
3.7
Summary
68
4
Sentence Subjectivity and Sentiment Classification
70
4.1
Subjectivity
72
4.2
Sentence Subjectivity Classification
73
4.3
Sentence Sentiment Classification
76
4.3.1
Assumption of Sentence Sentiment Classification
77
4.3.2
Classification Methods
78
4.4
Dealing with Conditional Sentences
80
4.5
Dealing with Sarcastic Sentences
82
4.6
Cross-Language Subjectivity and Sentiment Classification
84
4.7
Using Discourse Information for Sentiment Classification
86
4.8
Emotion Classification of Sentences
87
4.9
Discussion
88
5
Aspect Sentiment Classification
90
5.1
Aspect Sentiment Classification
91
5.1.1
Supervised Learning
92
5.1.2
Lexicon-Based Approach
93
5.1.3
Pros and Cons of the Two Approaches
96
5.2
Rules of Sentiment Composition
98
5.2.1
Sentiment Composition Rules
99
5.2.2
DECREASE and INCREASE Expressions
106
5.2.3
SMALL_OR_LESS and
LARGE__ORJVÍORE
Expressions
109
5.2.4
Emotion and Sentiment Intensity
112
5.2.5
Senses of Sentiment Words
112
5.2.6
Survey of Other Approaches
114
5.3
Negation and Sentiment
116
5.3.1
Negation Words
116
5.3.2
Never
119
Contents
vii
5.3.3
Some Other Common Sentiment Shifters
121
5.3.4
Shifted or Transferred Negations
122
5.3.5
Scope of Negations
122
5.4
Modality and Sentiment
123
5.5
Coordinating Conjunction But
127
5.6
Sentiment Words in Non-opinion Contexts
129
5.7
Rule Representation
131
5.8
Word Sense Disambiguation and Coreference Resolution
133
5.9
Summary
135
6
Aspect and Entity Extraction
137
6.1
Frequency-Based Aspect Extraction
138
6.2
Exploiting Syntactic Relations
140
6.2.1
Using Opinion and Target Relations
141
6.2.2
Using Part of and Attribute-of Relations
147
6.3
Using Supervised Learning
149
6.3.1
Hidden Markov Models
150
6.3.2
Conditional Random Fields
151
6.4
Mapping Implicit Aspects
153
6.4.1
Corpus· Based Approach
153
6.4.2
Dictionary-Based Approach
154
6.5
Grouping Aspects into Categories
157
6.6
Exploiting Topic Models
159
6.6.1
Latent Dirichlet Allocation
160
6.6.2
Using Unsupervised Topic Models
163
6.6.3
Using Prior Domain Knowledge in Modeling
168
6.6.4
Lifelong Topic Models: Learn as Humans Do
171
6.6.5
Using Phrases as Topical Terms
174
6.7
Entity Extraction and Resolution
179
6.7.1
Problem of Entity Extraction and Resolution
179
6.7.2
Entity Extraction
183
6.7.3
Entity Linking
184
6.7.4
Entity Search and Linking
185
6.8
Opinion Holder and Time Extraction
186
6.9
Summary
187
7
Sentiment Lexicon
Generation 189
7.1
Dictionary-Based Approach
190
7.2
Corpus-Based Approach
193
7.2.
і
Identifying Sentiment Words from a Corpus
19Ί
7.2.7,
Dealing with Context-Dependent Sentiment Words
195
7.2.3
Lexicon Adaptation
197
7.2.4
Some Other Related Work
198
7.3
Desirable and Undesirable Facts
199
7.4
Summary
200
viii
Contents
8
Analysis of Comparative Opinions
202
8.1
Problem Definition
202
8.2
Identify Comparative Sentences
206
8.3
Identifying the Preferred Entity Set
207
8.4
Special Types of Comparison
209
8.4.1
Nonstandard
Comparison
209
8.4.2
Cross-Type Comparison
211
8.4.3
Single-Entity Comparison
212
8.4.4
Sentences Involving Compare and Comparison
214
8.5
Entity and Aspect Extraction
215
8.6
Summary
216
9
Opinion Summarization and Search
218
9.1
Aspect-Based Opinion Summarization
219
9.2
Enhancements to Aspect-Based Summary
221
9.3
Contrastive
View Summarization
224
9.4
Traditional Summarization
225
9.5
Summarization of Comparative Opinions
225
9.6
Opinion Search
226
9.7
Existing Opinion Retrieval Techniques
227
9.8
Summary
229
10
Analysis of Debates and Comments
23 1
10.1
Recognizing Stances in Debates
232
10.2
Modeling Debates/Discussions
235
10.2.1
JTE Model
236
10.2.2
JTE-R Model: Encoding Reply Relations
240
10.2.3
JTE-P Model: Encoding Pair Structures
243
10.2.4
Analysis of Tolerance in Online Discussions
245
10.3
Modeling Comments
246
10.4
Summary
248
11
Mining Intentions
250
11.1
Problem of Intention Mining
250
11.2
Intention Classification
254
11.3
Fine-Grained Mining of Intentions
256
11
A Summary
258
12
Detecting Fake or Deceptive Opinions
259
12.1
Different Types of Spam
262
12.1.1
Harmful Fake Reviews
262
12.1.2
Types of Spammers and
Spamming
263
12.1.3
Types of Data, Features, and Detection
2,65
12.1.4
Fake Reviews versus Conventional Lies
267
12.2
Supervised Fake Review Detection
269
12.3
Supervised Yelp Data Experiment
272
Contents ix
12.3.1
Supervised Learning Using Linguistic
Features 273
12.3.2
Supervised Learning Using Bahavioral Features
274
12.4
Automated Discovery of Abnormal Patterns
275
12.4.1
Class Association Rules
276
12.4.2
Unexpectedness of One-Condition Rules
277
12.4.3
Unexpectedness of Two-Condition Rules
280
12.5
Model-Based Behavioral Analysis
282
12.5.1
Spam Detection Based on Atypical Behaviors
282
12.5.2
Spam Detection Using Review Graph
283
12.5.3
Spam Detection Using Bayesian Models
284
12.6
Group Spam Detection
285
12.6.1
Group Behavior Features
288
12.6.2
Individual Member Behavior Features
290
12.7
Identifying Reviewers with Multiple Uscrids
291
12.7.1
Learning in a Similarity Space
292
12.7.2
Training Data Preparation
293
12.7.3
d-Features and
s
-Features
294
12.7.4
Identifying Userids of the Same Author
295
12.8
Exploiting Burstiness in Reviews
298
12.9
Some Future Research Directions
300
12.10
Summary
301
13
Quality of Reviews
303
13.1
Quality Prediction as a Regression Problem
303
13.2
Other Methods
305
13.3
Some New Frontiers
306
13.4
Summary
307
14
Conclusions
309
Appendix
315
Bibliography
327
Index
363
|
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| id | DE-604.BV042277806 |
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| indexdate | 2024-07-10T01:17:07Z |
| institution | BVB |
| isbn | 9781107017894 |
| language | English |
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| oclc_num | 921905073 |
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| spelling | Liu, Bing 1963- Verfasser (DE-588)1014900026 aut Sentiment analysis mining opinions, sentiments and emotions Bing Liu, University of Illinois at Chicago New York Cambridge University Press 2015 XVI, 367 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Propositionale Einstellung (DE-588)4407952-7 gnd rswk-swf Information Extraction (DE-588)4566641-6 gnd rswk-swf Information Extraction (DE-588)4566641-6 s Propositionale Einstellung (DE-588)4407952-7 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027715231&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Liu, Bing 1963- Sentiment analysis mining opinions, sentiments and emotions Propositionale Einstellung (DE-588)4407952-7 gnd Information Extraction (DE-588)4566641-6 gnd |
| subject_GND | (DE-588)4407952-7 (DE-588)4566641-6 |
| title | Sentiment analysis mining opinions, sentiments and emotions |
| title_auth | Sentiment analysis mining opinions, sentiments and emotions |
| title_exact_search | Sentiment analysis mining opinions, sentiments and emotions |
| title_full | Sentiment analysis mining opinions, sentiments and emotions Bing Liu, University of Illinois at Chicago |
| title_fullStr | Sentiment analysis mining opinions, sentiments and emotions Bing Liu, University of Illinois at Chicago |
| title_full_unstemmed | Sentiment analysis mining opinions, sentiments and emotions Bing Liu, University of Illinois at Chicago |
| title_short | Sentiment analysis |
| title_sort | sentiment analysis mining opinions sentiments and emotions |
| title_sub | mining opinions, sentiments and emotions |
| topic | Propositionale Einstellung (DE-588)4407952-7 gnd Information Extraction (DE-588)4566641-6 gnd |
| topic_facet | Propositionale Einstellung Information Extraction |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027715231&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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