隨著社群媒體的盛行，使用者產生大量的文字資料，如：推文、部落格和評論等，這些文字資料都富含著潛在的情緒，我們可以透過情緒分析來得到人們的感受及意見取向。而近年來，情緒分析常以情緒詞典來當作分析的工具，由於領域的多樣性及領域的先驗知識，使得特定領域的情緒詞典在情緒分析中扮演著相當重要的角色。
目前中文的情緒詞典資源還是不足且多為不分領域的，因此我們透過建立特定領域的情緒詞典來輔助情緒分析，使結果更為準確。在本研究中，我們分析Booking.com中1,294,141則旅館評論，利用向量空間模型來得到字詞之間的語義關係，並預測字詞的情緒分數，將兩者結合後再透過標籤傳播法以自動建置旅館領域的中文情緒詞典。我們所提出的方法可以達到83%的準確度。

With the booming of social media, users generate a large number of texts, such as tweets, blogs, and comments, which are full of potential sentiment. Sentiment analysis aims to obtain people’s feelings and opinions from textual data. The most popular approach for sentiment analysis is to consult the sentiment lexicon. However, due to the diversity of the domain and the prior knowledge, the domain-specific sentiment lexicon plays an important role in sentiment analysis.
Chinese sentiment lexicon resources, when compared to their English counterparts, are still limited and mostly for general-purpose. Therefore, this research proposes techniques to construct a domain-specific sentiment lexicon in order to obtain a more accurate sentiment analysis. In this thesis, we analyze 1,294,141 hotel reviews crawled from Booking.com, utilizing the vector space model to obtain the semantic meanings between words, and predicting the sentiment scores of the words. Finally, we combine the context and sentiment information with label propagation method to construct a domain-specific sentiment lexicon automatically in hotel domain. The method we proposed achieves 83% precision.

論文審定書 i
摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Problem 6
1.3 Research Motivation 6
1.4 Research Purpose 7
1.5 Thesis Organization 7
Chapter 2 Literature Review 9
2.1 Lexicon-based Sentiment Analysis 9
2.2 Adding Sentiment Information to Word 10
2.3 Expanding Sentiment Words Automatically 12
Chapter 3 Our Approach 15
3.1 Overall Process 15
3.2 Data Collection 16
3.3 Data Preprocessing 17
3.3.1 Data Cleaning 17
3.3.2 Segmentation, tokenization and Part-of-Speech Tagging 18
3.4 Generating Word Representations 19
3.5 Building Sentiment Prediction Model 24
3.6 Label Propagation 27
3.6.1 Label Propagation Algorithm 27
3.6.2 Label Propagation in batches 30
3.6.3 Seed Selection 35
Chapter 4 Evaluation 36
4.1 Dataset Construction 36
4.2 Parameter selection in our approach 37
4.3 Comparing with Other Methods 42
4.3.1 comparing methods without label propagation 42
4.3.2 comparing methods with label propagation 45
4.4 Uniqueness of our domain-specific sentiment lexicon 50
4.5 Short discussion in opposite polarity problem 52
Chapter 5 Conclusion 55
References 56
Appendix – Chinese Sentiment Lexicon Extracted from Booking.com 61
Positive words 61
Negative words 61

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