Opinion Mining In Information Retrieval

✍ Scribed by Surbhi Bhatia, Poonam Chaudhary, Nilanjan Dey

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 119
• Series: SpringerBriefs In Computational Intelligence
• Edition: 1st Edition
• Category: Library

✦ Synopsis

This book discusses in detail the latest trends in sentiment analysis,focusing on “how online reviews and feedback reflect the opinions of users and have led to a major shift in the decision-making process at organizations.” Social networking has become essential in today’s society. In the past, people’s decisions to buy certain products (and companies’ efforts to sell them) were largely based on advertisements, surveys, focus groups, consultants, and the opinions of friends and relatives. But now this is no longer limited to one’s circle of friends, family or small surveys;it has spread globally to online social media in the form of blogs, posts, tweets, social networking sites, review sites and so on. Though not always easy, the transition from surveys to social media is certainly lucrative. Business analytical reports have shown that many organizations have improved their sales, marketing and strategy, setting up new policies and making decisions based on opinion mining techniques.

✦ Table of Contents

Preface......Page 6
About This Book......Page 8
Contents......Page 11
About the Authors......Page 14
1.1 Opinions: A Cognitive Source of Information......Page 16
1.1.1 Necessity for E-commerce: A New Trend in Online Shopping......Page 17
1.1.2 Facts and Opinions: Types of Opinion......Page 18
1.1.3 Demand for Information on Opinions......Page 19
1.2 Understanding Opinion Mining......Page 20
1.2.1 Definition of Opinion Mining by Various Researchers......Page 21
1.2.2 Levels of Opinion Mining......Page 22
1.2.3 Components of Opinion Mining......Page 24
1.2.4 Applications in Opinion Mining......Page 25
1.3 Steps in Opinion Mining......Page 26
1.3.3 Aspect Detection......Page 27
1.3.5 Opinion Summarization......Page 28
1.4 Information Retrieval: Challenges in Mining Opinions......Page 29
1.4.2 Present Opinion Mining Systems......Page 31
1.4.3 Challenges: Factors that Make Opinion Mining Difficult......Page 32
1.4.4 Our Charge and Approach......Page 33
References......Page 34
2 Opinion Score Mining System......Page 38
2.1 Framework Design......Page 39
2.1.3 Opinion Classification......Page 40
2.2 Opinion Score Mining System (OSMS)......Page 41
2.2.1 Opinion Crawling and Pre-processing Opinions......Page 44
2.2.2 Aspect Identification and Classification of Opinions......Page 45
2.2.3 Aspect Based Opinion Summarization......Page 46
2.2.4 Look up for Alternate Data......Page 47
References......Page 49
3.1 Introduction......Page 50
3.2 Extraction of Opinions from Text......Page 51
3.2.2 Challenges in Retrieving Opinion......Page 52
3.2.3 Existing Opinion Retrieval Techniques......Page 53
3.3 Opinion Spam Detection......Page 54
3.3.1 Spam Types......Page 55
3.3.3 Spam Detection Methods......Page 56
3.4 Cleaning Opinions......Page 58
3.4.1 Preprocessing and Its Tasks......Page 60
3.5 Crawling Opinions......Page 62
References......Page 67
4.1 Product Features Mining......Page 70
4.2 Opinion Word Extraction......Page 71
4.3 Features Opinion Pair Generation......Page 75
References......Page 76
5.1 Sentiment Analysis and Opinion Classification......Page 78
5.1.1 Problem in AI Context......Page 79
5.1.3 Sentence Subjectivity......Page 80
5.2.1 Dictionary-Based Approaches......Page 81
5.2.2 Machine Learning Techniques......Page 82
5.2.4 What’s Ahead......Page 84
5.3 Deep Learning in Opinion Mining......Page 85
5.4 Aspect-Based Opinion Classification......Page 89
References......Page 92
6.1 Text Summarization......Page 96
6.1.1 Extractive Summarization......Page 97
6.1.2 Abstractive Summarization......Page 98
6.2 Traditional Approaches......Page 99
6.2.1 Supervised Learning Techniques......Page 100
6.2.2 Unsupervised Learning Techniques......Page 101
6.3.1 Opinion Aggregation......Page 102
6.4 Aspect-Based Opinion Summarization......Page 104
References......Page 107
7 Conclusions......Page 111
7.1 Tools and Techniques......Page 112
7.2 Datasets and Evaluations......Page 113
7.3 Future Work......Page 117
References......Page 118

✦ Subjects

Computational Intelligence