Web opinion mining on consumer reviews.

• Other Authors: Wong, Yuen Chau.
• Format: Others
• Language: English; Chinese
• Published: 2008
• Subjects: Data mining > Mathematical models; World Wide Web; Consumers' preferences; Consumer satisfaction
• Online Access: http://library.cuhk.edu.hk/record=b5893776; http://repository.lib.cuhk.edu.hk/en/item/cuhk-326561

Wong, Yuen Chau. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. === Includes bibliographical references (leaves 80-83). === Abstracts in English and Chinese. === Chapter 1 --- Introduction --- p.1 === Chapter 1.1 --- Overview --- p.1 === Chapter 1.2 --- Motivation --- p.3 === Chapter 1.3 --- Objective --- p.5 === Chapter 1.4 --- Our contribution --- p.5 === Chapter 1.5 --- Organization of the Thesis --- p.6 === Chapter 2 --- Related Work --- p.7 === Chapter 2.1 --- Existing Sentiment Classification Approach --- p.7 === Chapter 2.2 --- Existing Sentiment Analysis Approach --- p.9 === Chapter 2.3 --- Our Approach --- p.11 === Chapter 3 --- Extracting Product Feature Sentences using Supervised Learning Algorithms --- p.12 === Chapter 3.1 --- Overview --- p.12 === Chapter 3.2 --- Association Rules Mining --- p.13 === Chapter 3.2.1 --- Apriori Algorithm --- p.13 === Chapter 3.2.2 --- Class Association Rules Mining --- p.14 === Chapter 3.3 --- Naive Bayesian Classifier --- p.14 === Chapter 3.3.1 --- Basic Idea --- p.14 === Chapter 3.3.2 --- Feature Selection Techniques --- p.15 === Chapter 3.4 --- Experiment --- p.17 === Chapter 3.4.1 --- Data Sets --- p.18 === Chapter 3.4.2 --- Experimental Setup and Evaluation Measures --- p.19 === Chapter 3.4.1 --- Class Association Rules Mining --- p.20 === Chapter 3.4.2 --- Naive Bayesian Classifier --- p.22 === Chapter 3.4.3 --- Effect on Data Size --- p.25 === Chapter 3.5 --- Discussion --- p.27 === Chapter 4 --- Extracting Product Feature Sentences Using Unsupervised Learning Algorithms --- p.28 === Chapter 4.1 --- Overview --- p.28 === Chapter 4.2 --- Unsupervised Learning Algorithms --- p.29 === Chapter 4.2.1 --- K-means Algorithm --- p.29 === Chapter 4.2.2 --- Density-Based Scan --- p.29 === Chapter 4.2.3 --- Hierarchical Clustering --- p.30 === Chapter 4.3 --- Distance Function --- p.32 === Chapter 4.3.1 --- Euclidean Distance --- p.32 === Chapter 4.3.2 --- Jaccard Distance --- p.32 === Chapter 4.4 --- Experiment --- p.33 === Chapter 4.4.1 --- Cluster Labeling --- p.33 === Chapter 4.4.2 --- K-means Algorithm --- p.34 === Chapter 4.4.3 --- Density-Based Scan --- p.35 === Chapter 4.4.4 --- Hierarchical Clustering --- p.36 === Chapter 4.5 --- Discussion --- p.37 === Chapter 5 --- Extracting Product Feature Sentences Using Concept Clustering --- p.39 === Chapter 5.1 --- Overview --- p.39 === Chapter 5.2 --- Distance Function --- p.40 === Chapter 5.2.1 --- Association Weight --- p.40 === Chapter 5.2.2 --- Chi Square --- p.41 === Chapter 5.2.3 --- Mutual Information --- p.41 === Chapter 5.3 --- Experiment --- p.41 === Chapter 5.3.1 --- Effect on Distance Functions --- p.42 === Chapter 5.3.2 --- Extraction of Product Features Clusters --- p.43 === Chapter 5.3.3 --- Labeling of Sentences --- p.45 === Chapter 5.4 --- Discussion --- p.48 === Chapter 6 --- Extracting Product Feature Sentences Using Concept Clustering and Proposed Unsupervised Learning Algorithm --- p.49 === Chapter 6.1 --- Overview --- p.49 === Chapter 6.2 --- Problem Statement --- p.50 === Chapter 6.3 --- Proposed Algorithm - Scalable Thresholds Clustering --- p.50 === Chapter 6.4 --- Properties of the Proposed Unsupervised Learning Algorithm --- p.54 === Chapter 6.4.1 --- Relationship between threshold functions & shape of clusters --- p.54 === Chapter 6.4.2 --- Expansion process --- p.56 === Chapter 6.4.3 --- Impact of Different Threshold Functions --- p.58 === Chapter 6.5 --- Experiment --- p.61 === Chapter 6.5.1 --- Comparative Studies for Clusters Formation and Sentences Labeling with Digital Camera Dataset --- p.62 === Chapter 6.5.2 --- Experiments with New Datasets --- p.67 === Chapter 6.6 --- Discussion --- p.74 === Chapter 7 --- Conclusion and Future Work --- p.76 === Chapter 7.1 --- Compare with Existing Work --- p.76 === Chapter 7.2 --- Contribution & Implication of this Work --- p.78 === Chapter 7.3 --- Future Work & Improvement --- p.79 === REFFERENCE --- p.80 === Chapter A --- Concept Clustering for DC data with DB Scan (Terms in Concept Clusters) --- p.84 === Chapter B --- Concept Clustering for DC data with Single-linkage Hierarchical Clustering (Terms in Concept Clusters) --- p.87 === Chapter C --- Concept Clusters for Digital Camera data (Comparative Studies) --- p.91 === Chapter D --- Concept Clusters for Personal Computer data (Comparative Studies) --- p.98 === Chapter E --- Concept Clusters for Mobile data (Comparative Studies) --- p.103 === Chapter F --- Concept Clusters for MP3 data (Comparative Studies) --- p.109