本研究提出一個以連結為基礎的分類方法來辨認部落格社群中，支持和反對某一特定議題的群組。我們將部落格中討論的互動行為建構成一個圖，參與其中的討論者被視為圖中的一個點；緊接著根據語意傾向來建立可能的相反意見連結，相反立場的討論者間會建立起一條連結。本研究使用最大切集的演算法來找出最有可能的正、反意見雙方。使用語意傾向的分類結果與使用本研究所提出簡單連結方法的分類結果作比較；另外，簡單連結方法的分類結果也與利用超圖形強化連結方法的結果作比較。

This thesis proposes a link-based approach to identify supporting and opposing groups in a Weblog community. We formulate the interaction behavior as a graph. Bloggers involved in the discussion of one specific issue are formulated as vertices. Semantic orientation is used to construct possible opposite opinion links. Bloggers with opposite stances will form an opposite link. A max-cut algorithm is used latter to obtain the optimal approximation of supporting and opposing groups. The categorization results are compared between semantic orientation classifier and simple link-based categorization. The simple link-based categorization compares then with the enhancement of link-based categorization using hypergraph.

Abstract iii
中文摘要 iv
Table of Contents v
Lists of Figures vii
Lists of Tables viii

Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation 2
1.3 Research Objectives 3
1.4 Thesis Organization 4
Chapter 2 Literature Review 5
2.1 Weblogs 5
2.2 Text-based Document Categorization 7
2.2.1 Preprocessing 8
2.2.2 Constructing Classifier 11
2.3 Graph Theory 13
2.3.1 Hypergraph 13
2.3.2 Bipartite Graph and Max-cut Algorithm 16
2.4 Semantic Orientation 17
Chapter 3 The Link-based Categorization 19
3.1 Problem Definition 19
3.2 Categorization Process 20
3.3 Data Collection and Preprocessing 23
3.4 Issue Clustering 26
3.5 Semantic Orientation 30
3.5.1 General Inquirer 31
3.5.2 Semantic Orientation Adaptation 32
Chapter 4 Enhancement and Max-cut Algorithm 36
4.1 Enhancement 36
4.1.1 Enhancing Algorithm 37
4.2 Max-cut Algorithm 41
Chapter 5 Experiment Results 44
5.1 Evaluating Criteria 44
5.2 Results 45
Chapter 6 Conclusion and Research Limitation 50
References 53
Appendix A. A POS Tag List 57
Appendix B. A List of The Top 300 Terms with The Highest tf*idf value 58
Appendix C. The List of the Issue Clustering Result 60
Appendix D. A List of General Inquirer for Categories of Positive and Negative 63

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