隨著互聯網技術的發展，特別是在網路應用程式中，新聞每一天都從世界各地的來源大量湧入網路。導致使用者幾乎不可能的完整的追蹤相關事件。基於這個問題，新聞服務和搜索引擎經變得越來越盛行。就算這些服務有辦法基於內容相似或類別來分類新聞，但是最終結果依然無法代表使用者的立場。本研究的主要目的是通過情感分析判斷使用者立場並提供不同的新聞分群法，並將其方法與傳統的新聞分群法比較。實驗後，我們可以看到利用情感分析的新聞分群法跟使用者回傳的資料有較高的相似性。因此，基於情感分的新聞分群法確實是一個可對應到使用者立場的方法。

With the evolution of Internet technologies, especially in Web applications, news articles flood the Web every day from an extreme amount of news portals from around the world. It is almost impossible for a single person to keep track of an event, or a series of related events. Based on this problem, news services and search engines have become more popular. Despite of the ability to organize the news based on content similarity or categories, the result can't represent user perspective. By all means, the main objective of this research is to propose a different news articles clustering method based on users' perspective by using sentiment analysis and compare it to traditional clustering method. After the experiments, we could see that clustering the news articles with sentiment analysis had a higher similarity according to users' real world usage in comparison to the traditional clustering. Hence, clustering news articles based on sentiment analysis is indeed a viable way to match users' perspective.

Abstract i
Acknowledgement ii
List of Figures v
1 Introduction 1
2 Related Work 6
2.1 Hierarchical clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 Text classification approach . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 Lexicon based approach . . . . . . . . . . . . . . . . . . . . . . . 9
3 Methodology 10
3.1 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.2 Keyword Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1.3 Complete Linkage Clustering . . . . . . . . . . . . . . . . . . . . 13
3.2 Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 Experiments 17
4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1.2 User system setup . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1.3 Evaluation method . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2.1 Clustering title . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.2 Clustering first paragraph . . . . . . . . . . . . . . . . . . . . . . . 25
4.2.3 Clustering last paragraph . . . . . . . . . . . . . . . . . . . . . . . 28
4.2.4 Overall performance comparison . . . . . . . . . . . . . . . . . . . 31
5 Conclusion 32
References 33

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