隨著電子商務的快速發展，網際網路已經成為收集各類型顧客評論的重要來源。
對於廠商來說，顧客評論可以幫助他們有效地瞭解使用者在購買產品之後的意見
回饋，也可得知所進行的行銷計畫是否產生預期的效益。此外，這些顧客評論也
可幫助廠商瞭解個別使用者的偏好，進而針對其偏好採取較有效率的行銷方式。
過去的資料探勘研究主要著重在市場的人口統計數據、使用者態度、性格與交易
行為的分析，利用這些分析的結果，可用以輔助行銷決策。然而，顧客評論在資
料探勘的範疇當中，受到的關注仍然有限。因此，本研究提出一套有效的產品特
性擷取技術，用以提升顧客評論自動摘要的效能。實證結果顯示，本研究所提出
的擷取技術，較現有的技術有著更佳的擷取效能以及穩定度。
關鍵字: 文件探勘、顧客評論、意見摘要、語義傾向

With the rapid expansion of e-commerce, the Web has become an excellent source for
gathering customer opinions (or so-called customer reviews). Customer reviews are
essential for merchants or product manufacturers to understand general responses of
customers on their products for product or marketing campaign improvement. In
addition, customer reviews can enable merchants better understand specific
preferences of individual customers and facilitates making effective marketing
decisions. Prior data mining research mainly concentrates on analyzing customer
demographic, attitudinal, psychographic, transactional, and behavioral data for
supporting customer relationship management and marketing decision making and did
not pay attention to the use of customer reviews as additional source for marketing
intelligence. Thus, the purpose of this research is to develop an efficient and effective
opinion summarization technique. Specifically, we will propose a semantic-based
product feature extraction technique (SPE) which aims at improving the existing
product feature extraction technique and is desired to enhance the overall opinion
summarization effectiveness.

Chapter 1 Introduction................................................................................................1
1.1 Background ................................................................................................1
1.2 Research Motivation and Objective ...........................................................4
1.3 Organization of the Thesis .........................................................................6
Chapter 2 Literature Review......................................................................................8
2.1 Existing Opinion Summarization Technique .............................................8
2.1.1 Product Feature Extraction.............................................................9
2.1.2 Opinion Orientation Identification...............................................12
2.1.3 Opinion Summary Generation.....................................................13
2.2 Limitations ...............................................................................................13
Chapter 3 Design of Semantic-Based Product Feature Extraction (SPE)
Technique ....................................................................................................................15
3.1 Preprocessing ...........................................................................................16
3.2 Frequent Feature Identification and Feature Pruning ..............................18
3.3 Semantic-Based Refinement ....................................................................18
Chapter 4 Empirical Evaluation...............................................................................23
4.1 Data Collection ........................................................................................23
4.2 Evaluation Criteria ...................................................................................24
4.3 Comparative Evaluation...........................................................................25
4.4 Sensitivity to Minimum Supports ............................................................29
4.5 Effects of Use of Positive/Negative Verbs in SPE...................................31
Chapter 5 Conclusion and Future Research Directions .........................................34
References ....................................................................................................................36
Appendix A..................................................................................................................38

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