臺灣博碩士論文加值系統

隨著Web 2.0的發展，近年來情感分析也成為相當熱門的研究議題。情感分析的目的即是以自動化的方式獲取電子文本中文字所隱含的情感態度，進而辨識電子文本所欲表達的情感傾向，其中，SentiWordNet即為情感分析中相當重要的情感詞彙資源。SentiWordNet是以WordNet為基礎所發展出的情感詞彙庫，該詞彙庫會賦予WordNet中的每個同義詞集三組分別代表正向、負向與中立的情感極性分數，而情感分析的進行即可透過辨識該詞彙庫所賦予詞彙的情感分數進而對電子文本進行分類。然而SentiWordNet雖可幫助情感分析進行，但仍存在字義辨識的缺點，由於詞彙普遍存在一字多義之問題，在情感分析進行中如何挑選詞彙正確之字義也將影響情感分析成效，而過去使用SentiWordNet進行情感分析的相關研究中，少有學者探討字義辨識問題對情感分析結果的影響。因此，本研究即以SentiWordNet為基礎，透過相關字義辨識方法建置具有領域特性之情感子詞彙庫，用以幫助情感分析中多義詞字義的選擇，並藉由改善SentiWordNet的字義辨識問題，進而提升情感分析的成效。實驗結果證實，相較於使用SentiWordNet進行情感分析，使用本研究改善字義辨識問題後所建置的具領域特性情感詞彙庫，確實能夠提升其分類準確性。

With the development of Web 2.0, sentiment analysis has become a popular research topic in recent years. The goal of sentiment analysis is using an automated method to get implicit sentiment attitudes and correctly identifies the articles to the corrective sentiment orientations. SentiWordNet is an important vocabulary resource in the sentiment analysis. SentiWordnet is based on WordNet. SentiWordNet gives each synset positive, negative, objective sentiment scores. Sentiment analysis can classify the digital text by sentiment score which the synset of SentiWordNet gives. Although, SentiWordNet can help the process of sentiment analysis, it exists disadvantages of word sense disambiguation, which the sense of the word has multiple meanings and further to affect the result of sentiment analysis. In literature, few scholars discuss the problem in the academic field of sentiment analysis. This thesis builds a domain-oriented sentiment lexicon based on SentiWordNet to choice the sense of the word in sentiment analysis, improve word sense disambiguation of SentiWordNet and further to increase the accuracy of sentiment analysis.

目錄
摘要I
AbstractII
誌謝辭III
目錄IV
圖目錄VII
表目錄VIII
第一章、 緒論1
1.1 研究背景與動機1
1.2 研究問題3
1.3 研究目的3
1.4 研究範圍4
1.5 論文架構4
第二章、 文獻探討6
2.1 情感分析(Sentiment Analysis)6
2.1.1 主觀性分類(Subjectivity Classification)6
2.1.2 情感分類(Sentiment Classification)6
2.1.3 情感分析流程7
2.2 字義辨識(Word Sense Disambiguation)11
2.2.1 監督式學習法(Supervised Disambiguation)11
2.2.2 非監督式學習法(Unsupervised Disambiguation)12
2.2.3 知識為基礎方法(Knowledge-Based Disambiguation)12
2.3 WordNet詞彙庫13
2.4 SentiWordNet情感詞彙庫13
2.5 小結15
第三章、 研究方法16
3.1 研究架構16
3.2 文章資料預處理18
3.2.1 電子文本18
3.2.2 文章斷句19
3.2.3 詞性標記19
3.2.4 字詞還原20
3.2.5 去除無用字(StopWord)21
3.3 情感子詞彙庫建置模組22
3.3.1 特徵詞彙擷取22
3.3.2 詞彙字義領域性計算24
3.3.3 詞彙字義Sense排序28
3.3.4 門檻值篩選28
3.3.5 建立具領域特性情感子詞彙庫29
3.4 情感文章分類29
3.5 文章情感分類評估30
3.6 小結32
第四章、 實驗結果與評估33
4.1 實驗說明33
4.1.1 實驗資料集與驗證資料集33
4.1.2 特徵詞彙擷取33
4.1.3 建置情感子詞彙庫34
4.1.4 評估方式35
4.2 實驗結果36
4.2.1 實驗一36
4.2.2 實驗二41
第五章、 結論與未來展望44
5.1 研究結論44
5.2 研究貢獻45
5.3 未來研究方向45
參考文獻47
附錄 無用字與雜訊詞列表51

圖目錄
圖1-1研究流程圖 4
圖2-1情感分析流程圖 7
圖2-2 SVM示意圖11
圖3-1研究流程圖 17
圖3-2文章預處理流程圖18
圖3-3情感子詞彙庫建置模組流程圖 22
圖3-4詞彙字義與文章相似度計算流程圖 26
圖3-5建立情感子詞彙庫示意圖-以Suck為例 29
圖4-1實驗一Part1結果比較38
圖4-2實驗一Part2結果比較40

表目錄
表3-1 Brill Tagger詞性符號對照表19
表3-2詞性標註轉換對照表 20
表3-3形容詞詞性下之詞彙"bad"於SentiWordNet之Sense與例句節錄表25
表3-4文章情感向量模型30
表4-1情感子詞彙庫組合對照表 35
表4-2實驗一Part1數據資訊36
表4-3實驗一Part1分類結果37
表4-4實驗一Part2數據資訊39
表4-5實驗一Part2分類結果39
表4-6實驗二Part1分類結果41
表4-7實驗二Part1成對樣本T檢定42
表4-8實驗二Part2分類結果42
表4-9實驗二Part2成對樣本T檢定42

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