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E-grāmata: Review Comment Analysis For E-commerce

(East China Normal Univ, China), (East China Normal Univ, China), (East China Normal Univ, China), (East China Normal Univ, China), (East China Normal Univ, China)
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Review Comment Analysis For E-commercePalielināt
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This book presents the recent achievements on the processing of representative user generated content (UGC) on E-commerce websites. This large size of UGC is valuable information for data mining to help customer/object profiling. It provides a comprehensive overview on the concept of customer credibility, object-oriented review summarization technology and content-based collaborative filtering algorithm. It covers a feedback mechanism which is designed to discover customer credibility, which is used to define the professional degree of review content; product-oriented review summarization for restaurants or trip arrangements, and introduced content-based collaborative filtering for product recommendation.
Preface ix
Acknowledgments xiii
1 Introduction
1(18)
1.1 Background
1(1)
1.2 Challenges
2(7)
1.3 Related Work
9(8)
1.4 Outline of Book Content
17(2)
2 Credibility Learning
19(26)
2.1 Problem Definition
19(5)
2.1.1 Problem Description
20(1)
2.1.2 Background
21(3)
2.2 Scoring Framework Overview
24(1)
2.3 Review Comment Analysis
25(4)
2.3.1 ME Model
26(1)
2.3.2 Constructing Labeled Data
27(2)
2.3.3 Training and Prediction
29(1)
2.4 Customer Credibility Calculation
29(4)
2.4.1 Product and Customer
30(2)
2.4.2 Shop and Customer
32(1)
2.4.3 Credibility Calculation
33(1)
2.5 Re-scoring
33(1)
2.6 Experimental Results
34(10)
2.6.1 Datasets
34(1)
2.6.2 Review Comment Analysis
35(3)
2.6.3 Product and Shop Re-scoring
38(6)
2.7 Conclusion
44(1)
3 Entity Resolution
45(30)
3.1 Problem Definition
45(2)
3.2 Learning-based Method on Centralized System
47(9)
3.2.1 Data Preprocessing
48(6)
3.2.2 Entity Resolution
54(2)
3.3 Random-based Method on Distributed System
56(7)
3.3.1 Framework Introduction
57(1)
3.3.2 Entity Signature Generation
58(1)
3.3.3 Candidate Pair Generation
59(2)
3.3.4 Redundancy Reduction
61(2)
3.4 Experimental Results
63(11)
3.4.1 Results of Learning-based Method
63(4)
3.4.2 Results of Random-based Method
67(7)
3.5 Conclusion
74(1)
4 Review Selection
75(14)
4.1 Problem Definition
75(1)
4.2 Quality and Diversity of Review Set
76(4)
4.2.1 The Quality of Review Set
76(2)
4.2.2 The Diversity of Review Set
78(2)
4.3 Review Selection Algorithm
80(1)
4.3.1 Opinion Extraction
80(1)
4.3.2 Review Selection
81(1)
4.4 Experimental Results
81(7)
4.4.1 Dataset and Settings
81(2)
4.4.2 Evaluation on Diversity Factor
83(3)
4.4.3 Comparison of Algorithms
86(2)
4.5 Conclusion
88(1)
5 Review Summarization
89(38)
5.1 Problem Definition
89(3)
5.1.1 Diversify-based Summary Generation
90(1)
5.1.2 Topic Model-based Summary Generation
91(1)
5.2 Diversity-based Approach
92(7)
5.2.1 Finding Evaluative Snippets
92(2)
5.2.2 Predicting Snippet Scores
94(1)
5.2.3 Summarizing Product Snippets
95(4)
5.3 Topic Model-based Approach
99(10)
5.3.1 Bilateral Topic Model
100(4)
5.3.2 Inference Algorithm
104(4)
5.3.3 Summarization
108(1)
5.4 Experimental Results
109(15)
5.4.1 Dataset
110(1)
5.4.2 Results of Diversity-based Approach
111(6)
5.4.3 Results of Topic Model-based Approach
117(7)
5.5 Conclusion
124(3)
6 Recommendation
127(14)
6.1 Background
127(2)
6.1.1 Traditional Methods
127(1)
6.1.2 Topic Analysis
128(1)
6.2 Comment-based Collaborative Filtering Model
129(6)
6.2.1 Profile Generator
130(1)
6.2.2 Representation of Samples
131(2)
6.2.3 Prediction Models
133(1)
6.2.4 Enhanced Systems
134(1)
6.2.5 Representative Review Selection
134(1)
6.3 Experimental Results
135(4)
6.3.1 Dataset
136(1)
6.3.2 Evaluation Metric
136(1)
6.3.3 Baselines
137(1)
6.3.4 Topic Analysis
137(1)
6.3.5 Main Results
138(1)
6.3.6 Further Analysis
139(1)
6.4 Conclusion
139(2)
7 Conclusion
141(2)
Bibliography 143(10)
Index 153
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