臺灣博碩士論文加值系統

在情感分析領域之中，如何將電子本文準確分類至正確的情感傾向，一直是熱門的研究議題之一。若能準確將這些網路上的產品評論文章、網誌內容作情感喜好或厭惡的分類時，將能創造出更多的應用發展，像是提供企業在產品或服務上的競爭分析、營銷分析…等。或給予使用者在查詢一事件或議題上，能快速提供大眾意見，讓使用者能迅速了解公共議題上的情感傾向。SentiWordNet為一在情感分析內重要的情感詞彙資源，以WordNet為基礎將詞彙給予情感數值來表示該詞彙的情感程度，分別給予每個詞彙在不同情況下正面、負面與中立三個傾向的情感數值，透過詞彙的情感數值便能瞭解該詞彙具有何種情感。然而，在過去情感分析的研究中，著重於如何建立與找尋出情感詞彙資源，或使用已建立好的情感詞彙進行情感分析。較少有學者探討SentiWordNet內中立詞彙對情感文章分類的影響，與依據不同領域重新修正中立詞彙情感數值，使其貼近該領域上的情感使用。因此，本研究提出以詞彙相關性方法來重新修正SentiWordNet的情感字典，改善SentiWordNet的情感字典內中立詞彙的情感數值，從中萃取出SentiWordNet情感數值錯誤或未能符合領域的詞彙，藉此來改善情感分類上的準確。

In the academic field of sentiment analysis, knowing how to correctly classify the articles to the corrective sentiment-orientation is a hot research topic. If we can correctly classify the critiques, we can create more applied development. Sentiment detection mainly detects useful information in the articles, from the articles concerning mining or extract useful information, including viewpoint, fancy, and attitude. Although, these articles are valuable, but a lot of articles are dispersal, if reading all articles will take more time and human. If we only read a few articles, this may cause prejudice. Therefore, automatic sentiment detection and classification has become one of hot topics of the day. SentiWordNet is important vocabulary resource in the sentiment analysis. SentiWordNet is based on WordNet. SentiWordNet gives three sentiment scores to each synset. Sentiment scores are representative sentiment intensities on the synset. Through the three sentiment scores one can know the sentiment-orientation of word. However, in the past, sentiment analysis of the study focuses on how to build vocabulary resources and find out the articles that are sentiment-oriented, or use the sentiment vocabulary resources classifying the articles. Few scholars in the academic field of sentiment analysis have explored sentiment objective words influential sentiment classification. Therefore, this study uses vocabulary relevance to revise SentiWordNet objective sentiment score and improve the text classification.

目錄
摘要 I
Abstract II
誌謝辭 III
目錄 IV
圖目錄 V
表目錄 VI
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 研究流程說明 4
第二章、文獻探討 6
2.1 情感分析 6
2.2 支援向量機 14
2.3 SentiWordNet 15
2.4 小結 20
第三章、研究方法 21
3.1. 文章預處理 22
3.2. 情感字典預處理 26
3.3. 情感詞彙相關性萃取法 28
3.4. 情感文章分類 32
3.5. 文件情感分類評估 34
3.6. 小結 35
第四章、實驗結果 36
4.1 實驗說明 36
4.2 實驗結果與分析 40
第五章、結論與未來展望 46
5.1 結論 46
5.2 未來研究方向 46
參考文獻 48
附錄 51


圖目錄
圖1- 1研究流程圖 4
圖2- 1兩極的形容詞架構 7
圖2- 2形容詞good、bad距離測量 8
圖2- 3情感分析基本流程圖 9
圖2- 4 SVM分類示意圖 14
圖3- 1系統架構圖 21
圖3- 2文章預處理流程圖 22
圖3- 3情感字典預處理流程圖 26
圖3- 4情感詞彙相關性萃取法流程圖 28
圖3- 5情感文章分類流程圖 32
圖4- 1 SentiWordNet詞彙內容 36
圖4- 2向量空間模型輸入資料內容 38
圖4- 3文章切割 38
圖4- 4實驗一、分類結果比較 40
圖4- 5實驗二、分類結果比較 42


表目錄
表2- 1 SentiWordNet資料結構 16
表2- 2 SentiWordNet情感數值統計分佈 17
表3- 1 Brill tagger詞性標註 23
表3- 2詞性標註轉換對照表 25
表3- 3情感文章分類向量空間模型 33
表3- 4混亂矩陣 34
表4- 1 SVM設定參數 37
表4- 2實驗一數據資料 40
表4- 3實驗一分類準確率 41
表4- 4實驗二SVM維度資料 42
表4- 5實驗二分類準確率 43
表4- 6實驗三分類準確率 44
表4- 7成對樣本T檢定 45

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