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
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_326561 |
| Date | January 2008 |
| Contributors | Wong, Yuen Chau., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, vi, 114 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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