近年來食品安全問題層出不窮，接連爆發塑化劑、毒澱粉、假油一連串的事件。然而，相關報導的數量龐大，一般民眾難以有效閱讀完所有資訊；再者，一連串的事件都與食品安全相關，社會是否從過去事件中學習到經驗，並在類似事件發生時做出不同的因應也是值得探討的議題。
但新聞閱聽者難以從非結構化的訊息中了解事件之間社會反應的差異，因此本研究的目的在於自動化分析同一主題的多個事件，探討社會對新聞事件的反應。
本研究旨在提出一個自動化的文本分析系統，自動分析隸屬同一主題的多個新聞事件。首先，本研究透過分群技術(Clustering)，以事件發展階段及利害關係人二維向度，呈現各利害關係人在事件各階段的言論內容。再者，系統將透過摘要技術(summarization)萃取事件發展重點以提供單一事件發展的新聞摘要。最後，以焦點訪談法(ORID)衡量系統的有效性，並同時探索讀者對於事件的反應。
藉由本研究提出的自動化文本分析系統，一般民眾可以更快速及有效的了解新聞事件的發展，回顧事件發生當下的感受、想法與行動。

In recent years, the crisis of food safety events continued happened in interval. There are three main food safety events, in sequence, “Plasticizer”, “Poison starch” and “Fake oil”. However, the related news reports are too enormous to be digested efficiently by the readers. In addition, it’s interested to know if similar events happen again, would they learn something from the past experiences and responds in a different way.
This study aimed to propose a system that can automatic analyze the related news belonging to the same topic. First, this study presents the opinions of each stakeholder on each period of the news development by clustering. Second, this system extracts the important content of news reports using summarization and provides the summarization of each news event to readers. Finally, this study combines the system with Focused Conversation Method (ORID) to evaluate the effective of the system and to explore the response of readers to the news events.
With the facility of the system that we proposed, the readers can understand the development of news event efficiently and recall their feeling, thought, and reaction for the news events at the moment that the event happened.

Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation 3
1.3 Research Objectives 4
Chapter 2 Literature Review 5
2.1 Automatic Content Analysis 5
2.2 Text Summarization 6
2.3 Clustering Algorithm 8
2.3.1 Hierarchical Cluster Analysis (HCA) 8
2.3.2 Other Clustering Methods 9
2.4 Focused Conversation Method (ORID) 11
Chapter 3 System Framework and Methodology 14
3.1 Definition 15
3.2 System Architecture 16
3.3 Data Acquisition 18
3.4 Preprocessing 19
3.4.1 Word segmentation 19
3.4.2 Term Aggregation 19
3.4.3 Feature Selection 21
3.5 Opinion Extraction 22
3.6 Clustering 27
3.7 Summarization 28
3.8 Content Analysis 28
Chapter 4 System Implementation and Results 30
4.1 Data Source 30
4.2 System Implementation 30
4.3 Results 33
Chapter 5 Evaluation and Results 39
5.1 Evaluation Design 39
5.2 Evaluation Results 42
5.2.1 The understanding of news events 42
5.2.2 The change of response of each reader for three events 44
5.2.3 The change of response of stakeholders for three events 46
5.3 Discussions 50
Chapter 6 Conclusion and Future Work 51
References 53
Appendix A. Contents Presented to Subject in Round 1 57
Appendix B. Summarization Results and Contents Presented to Subject in Round 2 60
Appendix C. ORID Interview Transcript in Round 1 64
Appendix D. ORID Interview Transcript in Round 2 72
Appendix E. Opinions of Stakeholders Cross Three Events 84

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