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研究生:陳琦宇
研究生(外文):Chi-Yu Chen
論文名稱:改良式貝氏分類器在情緒分類之研究
論文名稱(外文):An Improved Naïve Bayes Classifier for Sentence-based Opinion Classification
指導教授:邱昭彰邱昭彰引用關係
指導教授(外文):Chaochang Chiu
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:32
中文關鍵詞:意見探勘情緒分析多項式貝氏分類
外文關鍵詞:Opinion miningsentiment analysisNaïve Bayes Classifier
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在文件分類的領域中貝氏分類已被廣泛的使用。本研究針對網路評論提出了改良式貝氏分類器,目的是從大量且非結構化的評論中判斷其中評論的情緒分類。有別於傳統的貝氏分類器(NBC)在計算情緒特徵詞機率值的問題。本研究提出了結合啟發式規則與貝氏分類器Integrated Heuristic Rules and Naïve Bayes classifier (IHRNBC)提升在情緒傾向判定時的分類效果。本研究應用在中文論壇的3C科技與美容保養兩個領域。實驗結果顯示,本研究所提出的方法(IHRNBC)在分類準確率上優於現有的分類器,如傳統的貝氏分類 NBC與支持向量機(SVM)。
The Naïve Bayes classifier (NBC) has been often used in classifying documents. Accordingly, this study proposes an improved Naïve Bayes classifier in an attempt to conduct sentiment orientation classification on a mass volume of unstructured online comments. The classifier proposed in this study is different from the general NBC, whose disadvantage is that it cannot handle the feature of different classes in calculating probability. Therefore, this research proposes an Integrated Heuristic Rules and Naïve Bayes classifier (IHRNBC) to enhance the classification performance of sentiment orientation in opinion mining. The proposed classifier is applied for the contents taken from customer reviews of 3C and beauty products posted on online Chinese forums. Results show that the proposed classifier performs better than existing classifiers such as the general NBC and the Support Vector Machine (SVM).
改良式貝氏分類器在情緒分類之研究 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
致謝 vi
目錄 viii
表目錄 ix
圖目錄 x
第一章、緒論 1
第二章、文獻探討 4
2.1 意見探勘(Opinion Mining) 4
2.2 中文斷詞 6
2.3 特徵值選取 6
2.4 貝氏文件分類 7
第三章、研究方法與設計 10
3.1. 啟發式規則Module 10
3.2. 文章前處理處理 14
3.2.1 斷句處理 14
3.2.2 中文斷詞處理 15
3.2.3 停用字處理與特徵詞選取 16
3.3. Integrated Heuristic Rules and Naïve Bayes classifier (IHRNBC) 18
第四章、實驗結果 21
4.1 研究限制與假設 21
4.2 資料描述 21
4.3 分類效能指標 22
4.4 實驗結果 22
第五章、討論 26
第六章、結論與未來展望 28
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