Web 2.0的興起，帶動大眾言論的新浪潮：越來越多人在網際網路上發表想法、意見，甚至是對於特定商品的評論。在這麼大量的文字資料中，除了真實並且有參考價值的資料之外，也有可能包含惡意的垃圾訊息，或者是沒有意義的文字。因此，要如何判斷文字的品質變成了很重要的課題之一，這也成為文字探勘領域的熱門議題。
在過去的研究中通常只以文字詞語的特性作為分析的標的，但這樣的分析方式只能觀察到文字詞語的組合，或是評論整體長短等數據型的資料，並沒有辦法對於文章整體的架構及內容有所了解。修辭結構理論（Rhetorical Structure Theory, RST）透過分析子句和子句之間的關係，將文章轉換成包含階層特性的樹狀結構，讓我們對於整個文章結構有全面性的了解。我們加入修辭結構理論作為分析的標的，使文章品質分析的結果更精確，也更符合人類語言的特性。
我們提出一個文字品質分析流程，使用了NLTK（Natural Language Toolkit）進行自然語言處理，找出可能影響文字品質的影響因素，並使用不同的分類模型分析Amazon購物網站的真實使用者評論。我們比較了加入RST特徵前和加入後的結果，實驗證明我們提出的模型比起單純採用文字詞語的模型有顯著的進步。

The emergence of Web 2.0 has led to a new era of user generated content. More and more people tend to share their thinking, opinion and even user review of a specific product. Among all these textual data, not only useful paragraphs that are worth considering are in-cluded, there are also malicious comments and spams. Thus, how to determine the quality of text has become a serious problem, and a popular issue in the domain of text mining.
Most of the existing studies use the characteristic of tokens as the analysis target, which cannot represent the comprehensive structure of a document, but only the combination of words, or statistical data such as the length of the document. Rhetorical Structure Theory (RST) transform a document to a tree that have the characteristic of level according to the relation between text spans. By incorporating the concept of RST, we can have a more accu-rate result on analyzing text quality.
We proposed a process to analyze the quality of text, using NLTK (Natural Language Toolkit) to process the text, finding the potential predictor, and use different classification model to analyze real user reviews from Amazon.com. Also, an evaluation process is con-ducted to compare the result before and after adding RST features. Experiment shows that our model outperforms previous models which only consider the structure of word tokens.

Chapter 1 -Introduction 1
1.1 Background and Motivation 1
1.2 Research Purpose 2
1.3 Expected Results and Contribution 2
1.4 Thesis Organization 3
Chapter 2 -Literature Review 4
2.1 Rhetorical Structure 4
2.2 Rhetorical Structure Theory In The Domain Of Text Mining 8
2.3 Quality analysis 9
Chapter 3 -Problem Definition 11
3.1 Preliminaries 11
3.2 Problem Description 13
Chapter 4 -Research Approach 15
4.1 Research Architecture 15
4.2 Data Preparation 17
4.3 Natural Language Processing 19
4.4 Discourse Parsing 20
4.5 Feature Building 24
4.6 Quality Determination 28
Chapter 5 -Experiments 30
5.1 Dataset 30
5.2 Experimental Design 33
5.3 Experiment Result 34
5.3.1 NLP/ Discourse Parsing Result 34
5.3.2 Prediction of Quality 36
5.3.3 RST Weighting Scheme 38
Chapter 6 -Conclusion 43
Chapter 7 -References 45

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