Sentiment analysis and opinion mining:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
[San Rafael, California]
Morgan & Claypool
[2012]
|
| Series: | Synthesis lectures on human language technologies
16 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xiv, 165 Seiten Diagramme |
| ISBN: | 9781608458844 |
Staff View
MARC
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Record in the Search Index
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| adam_text | .:■■.■■:■■■■■. ..■;..... .■■■■;.■■. . .: ■■■.,:.;:■:. ■ ■.:■.:.■,,■ ■ ■
Contents
:
- ■■■ ■■ ■■ ■·
v.! ■ :* <p
Preface
.............................................................................................................xiii
1.
Sentiment Analysis: A Fascinating Problem
..........................................................1
1.1
Sentiment Analysis Applications.......
..................................................................2
1.2
Sentiment Analysis Research
............................................................................... 3
1.2.1
Different Levels of Analysis
.................................................................... 4
1.2.2
Sentiment Lexicon and Its Issues
............................................................ 5
1.2.3
Natural Language Processing issues........................................................
6
1.3
Opinion Spam Detection
.................................................................................... 7
1.4
What s Ahead
......................................................................................................7
2.
TheProblemof Sentiment Analysis
.....................................................................9
2.1
Problem Definitions
.......................................................................................... 10
2.1.1
Opinion Defintion
.................................................................................10
2.1.2
Sentiment Analysis Tasks
......................................................................14
2.2
Opinion Summarization.
...................................................................................17
2.3
Different Types of Opinions
..............................................................................18
2.3.1
Regular and Comparative Opinions
...................................................... 18
2.3.2
Explicit and Implicit Opinions
..............................................................19
2.4
Subjectivity and Emotion..........
........................................................................ 19
2.5
Author and Reader Standpoint
......................................................................... 21
2.6
Summary
........................................................................................................... 21
3.
Document
Sentiment
Classification,,
,„...„,......<,,„.„.,,„..................,.,.................. 23
3.1
Sentiment Classification Using Supervised Learning........................................
24
3.2
Sentiment Classification Using
U
nsupervised Learning
................................... 28
3.3
Sentiment Rating Prediction.......
...................................................................... 30
3.4
Cross-Domain Sentiment Classification
........................................................... 31
x
SENTIMENT
ANALYSIS AND OPINION MINING
3.5
Cross-Language Sentiment Classification
.........................................................
34
3.6
Summary
...........................................................................................................
35
4.
Sentence Subjectivity and Sentiment Classification
................♦...........................37
4.1
Subjectivity Classification
..................................................................................
3§
4.2
Sentence Sentiment Classification
.....................................................................
4j
4.3
Dealing with Conditional Sentences
.................................................................43
4.4
Dealing with Sarcastic Sentences
......................................................................44
4.5
Cross-Language Subjectivity and Sentiment Classification
..............................45
4.6
Using Discourse Information for Sentiment Classification
...............................47
4.7
Summary
...........................................................................................................47
5.
Aspect-Based Sentiment Analysis
......................................................................49
5.1
Aspect Sentiment Classification
........................................................................50
5.2
Basic Rules of Opinions and Compositional Semantics.........
...........................53
5.3
Aspect Extraction
..............................................................................................58
5.3.1
Finding Frequent Nouns and Noun Phrases
.........................................59
5.3.2
Using Opinion and Target Relations..............
.......................................61
5.3.3
Using Supervised Learning
...................................................................62
5.3.4
Using Topic Models.....
.........................................................................62
5.3.5
Mapping Implicit Aspects
.....................................................................66
5.4
Identifying Resource Usage Aspect
...................................................................67
5.5
Simutaneous Opinion Lexicon Expansion and Aspect Extraction
....................68
5.6
Grouping Aspects into Categories
.....................................................................71
5.7
Entity, Opinion Holder, and Time Extraction
..................................................73
5.8
Coreference Resolution and Word Sense Disambiguation
................................75
5.9
Summary
...........................................................................................................76
6.
Sentiment Lexicon Generation
..........................................................................79
6.1
Dictionary-Based Approach
..............................................................................80
6.2
Corpus-Based Approach
...................................................................................83
6.3
Desirable and Undesirable Facts.....
...................................................................87
6.4
Summary...
........................................................................................................88
7.
Opinion Summarization
....................................................................................91
7.1
Aspect-Based Opinion Summarization.
............................................................91
7.2
Improvements to Aspect-Based Opinion Summarization
.................................94
CONTENTS
7.3
Contrastive
View Summarization
......................................................................95
7.4
Traditional Summarization
................................................................................96
7.5
Summary
...........................................................................................................97
8.
Analysis of Comparative Opinions
.....................................................................99
8.1
Problem Definitions
..........................................................................................99
8.2
Identify Comparative Sentences
......................................................................102
8.3
Identifying Preferred Entities
..........................................................................103
8.4
Summary
.........................................................................................................105
9.
Opinion Search and Retrieval
..........................................................................107
9.1
Web Search vs. Opinion Search
...................................................................... 107
9.2
Existing Opinion Retrieval Techniques...
........................................................108
9.3
Summary
.........................................................................................................
Ill
10.
Opinion Spam Detection....
............................................................................ 113
10.1
Types of Spam and
Spamming.....................
...................................................114
10.1.1
Harmful Fake Reviews
........................................................................ 115
10.1.2
Individual and Group
Spamming..
...................................................... 115
10.1.3
Types of Data, Features, and Detection...............................................
116
10.2
Supervised Spam Detection
............................................................................. 117
10.3
Unsupervised Spam Detection
....................................................,...................120
10.3.1
Spam Detection Based on Atypical Behaviors
.................................... 120
10.3.2
Spam Detection Using Review Graph
................................................123
10.4
Group Spam Detection
...................................................................................124
10.5
Summary
.........................................................................................................125
11.
Quality of Reviews
..........................................................................................127
11.1
Quality as Regression Problem
........................................................................ 127
11.2
Other Methods
................................................................................................ 129
11.3
Summary.......
.................................................................................................. 130
12.
Concluding Remarks
...................................................................».,,„...,„,........ 133
.......................................................................................................... 135
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Sentiment
analysis and opinion mining is the field of study that analyzes people s opinions, sentiments,
evaluations, attitudes, and emotions from written language, It is one of the most active research areas in natural
language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this
research has spread outside of computer science to the management sciences and social, sciences due to its
importance to business and society as a whole. The growing importance of sentiment analysis coincides with
the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks.
For the first time in human history, we now have a huge volume of opinionated data recorded in digital
fon
л
for analysis.
Sentiment analysis systems are being applied in almost even business and social domain because opinions
are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions
of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this
reason, when we need to make a decision we often seek out the opinions of others. This is true not only for
individuals but also for organizations.
This book is a comprehensive introductory and survey text. It covers all important topics and the latest
developments in the field with over
400
references. It is suitable for students, researchers and practitioners
who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily
use it in class for courses on natural language processing, social media analysis, text mining, and data mining.
Lecture slides are also available online.
|
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| id | DE-604.BV040291574 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:20:55Z |
| institution | BVB |
| isbn | 9781608458844 |
| language | English |
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| physical | xiv, 165 Seiten Diagramme |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Morgan & Claypool |
| record_format | marc |
| series | Synthesis lectures on human language technologies |
| series2 | Synthesis lectures on human language technologies |
| spelling | Liu, Bing 1963- Verfasser (DE-588)1014900026 aut Sentiment analysis and opinion mining Bing Liu, University of Illinois at Chicago [San Rafael, California] Morgan & Claypool [2012] © 2012 xiv, 165 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on human language technologies 16 Information Extraction (DE-588)4566641-6 gnd rswk-swf Propositionale Einstellung (DE-588)4407952-7 gnd rswk-swf Information Extraction (DE-588)4566641-6 s Propositionale Einstellung (DE-588)4407952-7 s DE-604 Erscheint auch als Online-Ausgabe 978-1-6084-5885-1 Synthesis lectures on human language technologies 16 (DE-604)BV035447238 16 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=025146882&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 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=025146882&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Liu, Bing 1963- Sentiment analysis and opinion mining Synthesis lectures on human language technologies Information Extraction (DE-588)4566641-6 gnd Propositionale Einstellung (DE-588)4407952-7 gnd |
| subject_GND | (DE-588)4566641-6 (DE-588)4407952-7 |
| title | Sentiment analysis and opinion mining |
| title_auth | Sentiment analysis and opinion mining |
| title_exact_search | Sentiment analysis and opinion mining |
| title_full | Sentiment analysis and opinion mining Bing Liu, University of Illinois at Chicago |
| title_fullStr | Sentiment analysis and opinion mining Bing Liu, University of Illinois at Chicago |
| title_full_unstemmed | Sentiment analysis and opinion mining Bing Liu, University of Illinois at Chicago |
| title_short | Sentiment analysis and opinion mining |
| title_sort | sentiment analysis and opinion mining |
| topic | Information Extraction (DE-588)4566641-6 gnd Propositionale Einstellung (DE-588)4407952-7 gnd |
| topic_facet | Information Extraction Propositionale Einstellung |
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