臺灣博碩士論文加值系統

許多研究人員都致力於研究觀點分類，將網路上含有有用資訊的未結構化文章分類到正向或負向觀點裡。本研究與過去研究有三點不同的地方。第一，我們基於心理學裡的基本情感開發一個情感詞彙和三個分類器，SVM、SMO和Naive Bayes，以減少情感分類時的複雜度並且提供標準的情感類別。第二，我們根據讀者閱讀網路的文章所引發的情感去分類文章。最後，我們使用部落格文章去驗證分類器的概化能力。實驗結果顯示Information Gain和SVM 分別在特徵選擇法和機器學習法裡的績效是最好的。我們開發的分類器分類效果比過去研究所開發的分類器的分類效果還要好。這結果證明使用基本情感類別可以改善並提高情感分類器的分類效果。

Many researchers have devoted to study sentiment classification to classify unstructured texts with valuable information on the Web as positive or negative sentiments. This research differs from past studies in three points. First, we develop emotion and mood lexicons and three document classifiers, SVM, SMO, and Naive Bayes classifiers, based on basic emotions and moods defined in psychology to decrease the complexity of affective classification and provide standard affect categories. Second, we classify documents based on readers’ feelings induced by web articles. Finally, we use blog articles to verify the generalizability of the trained classifiers. The experimental results show that information gain and SVM perform better in feature selection and machine learning methods, respectively. Our classifiers outperform the past research and it proves that basic affect categories can be used to improve the performance of affect classifiers.

Chapter 1 Introduction
1.1 Research Background
1.2 Research Motivation
1.3 Research Objectives
Chapter 2 Related Works
2.1 Sentiment Classification
2.2 Emotion Classification
2.3 Basic Emotions and Moods
2.4 Feature Selection
2.5 Machine Learning Approaches to Text Classification
2.5.1 Support Vector Machine
2.5.2 SMO
2.5.3 Naive Bayes
Chapter 3 System Design
Chapter 4 System Evaluation and Result
4.1 Data Collection
4.2 Affect Category Mapping
4.3 Classifier Training
4.4 Classifiers Testing
4.4.1 Performance Measures
4.4.2 Testing Results
4.5 Evaluation of Generalizability
Chapter 5 Conclusion
References
Appendix A: The questionnaires of basic emotions
Appendix B: The Significance of Individual Emotion and Mood

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