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研究生:吳昆霖
研究生(外文):Kun-LinWu
論文名稱:基於概念延伸之情緒分類法
論文名稱(外文):Sentimental classification based on Concept expansion
指導教授:李昇暾李昇暾引用關係
指導教授(外文):Sheng-Tun Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工業與資訊管理學系專班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:54
中文關鍵詞:情緒分類直交表模糊正規概念分析概念延伸
外文關鍵詞:Sentimental ClassificationOrthogonalFuzzy Formal Concept AnalysisQuery Expansion
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隨著網際網路的蓬勃發展,其所帶來的龐大資訊量已經遠超過人們所能處理的範圍。知識時代的到來更是造成了電子化文件不斷的累積,因此透過文件分類技術,建構出一個具備自我學習能力的文件自動分類系統來協助使用者對大量的電子化文件進行分類,使其易於搜尋、組織以及有效運用在各領域也就成為一門有趣且富有挑戰性的議題。
由於部落格和社群網站的興起,使得我們越來越容易取得帶有使用者情緒的資訊,這些資訊裡的情緒字詞往往代表著不同情境下的情感反應。本研究將透過文件分類來預測未知情緒類別的文件集,藉此挖掘及瞭解文件所隱含的情緒與主觀性。其分類技術以模糊理論結合正規概念分析建立出能表達不確定資訊的情緒概念網路,用以整合可能會被忽略掉的情緒字詞,更加入概念延伸令整個情緒概念更為完整。另一方面也考量到分類準確率會因為參數調整而有相當大的差異,所以在進行分類之前,將透過田口品質工程的直交表設計反覆進行實驗以求得最高文件分類準確率的參數組合。
As the vigorous development of internet using, the huge amount of information which internet provides has already exceeded the level that people can easily handle. Knowledge era also leads to the great accumulation of electronic document. Hence, it becomes an interesting and challenging issue as to how to build a self-learning system that can automatically classify those electronic document through document classification technique in order to assist users in organization, search as well as effectively making use of these document in various areas.
Due to the rise of the blog and the social network site, it gets easier to have access to information which contains personal sentiment. This information usually means various emotional responses to different situations. This research will predict documents of unknown emotional category through document classification in order to explore and understand the connoted emotions and subjectivity. The classification technique will combine Fuzzy Logic with Formal Concept Analysis to create the concept lattice which can express uncertainty information. This concept lattice will help integrate some sentiment information which is easily ignored, and also add Query Expansion to make the emotion concept more complete. Furthermore, it is also considered that the classification precision may easily be affected by parameter adjustment; thus, before experiment, the researcher will conduct repeated experiment through the orthogonal of Taguchi method in order to acquire the parameter combination for the highest precision of document classification.
摘要 I
Abstract II
致謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究步驟與流程 3
1.4 論文架構 4
第二章 文獻探討與回顧 5
2.1 情緒分析 5
2.1.1 意見檢索 6
2.1.2 混合關鍵字與學習偵測方法 6
2.2 文件分類 7
2.2.1詞頻-逆向文件頻率 8
2.2.2 逆向一致頻率 9
2.2.3 一致性 10
2.2.4 文件分類技術 10
2.3 模糊正規概念分析 15
2.3.1 模糊理論 15
2.3.2 正規概念分析 16
2.3.3 正規情境 17
2.3.4 正規概念 18
2.3.5 概念網路 19
2.4 田口方法 21
2.4.1 品質損失函數 23
2.4.2 訊號雜音比 24
第三章 研究方法 25
3.1 概念學習 26
3.1.1 資料前處理 26
3.1.2 特徵選取 27
3.1.3 田口參數最佳化 28
3.1.4 文件概念化 30
3.1.5 概念延伸 33
3.2 分類流程 34
3.2.1 計算相似概念 34
3.2.2 推論最適類別 36
第四章 實驗與分析 37
4.1 實驗資料集 37
4.2 田口法直交表 38
4.3實驗結果 41
4.3.1 實驗1:Movie Review 41
4.3.2 實驗2:Amazon eBook Review 43
4.4 實驗結果比較 44
4.5 統計檢定 46
第五章 結論與未來展望 49
5.1 結論 49
5.2 未來展望 49
參考文獻 51

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