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研究生:吳俊諺
研究生(外文):Chun-Yen Wu
論文名稱:應用自我組織圖於社會網路文字訊息之情感分析
論文名稱(外文):Sentiment Analysis of Text Messages in Social Networks Based on Self-Organizing Maps
指導教授:楊新章楊新章引用關係
指導教授(外文):Hsin-Chang Yang
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:資訊管理學系碩士班
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:2013
畢業學年度:101
語文別:中文
論文頁數:56
中文關鍵詞:情感分析文本探勘自我組織圖
外文關鍵詞:Text MiningSentiment AnalysisSelf-Organizing Map
相關次數:
  • 被引用被引用:7
  • 點閱點閱:656
  • 評分評分:
  • 下載下載:151
  • 收藏至我的研究室書目清單書目收藏:3
隨著網際網路的發展和普及,以文本形式出現的訊息量也急遽地增長,對於其上資料分析的需求量也隨之增加。透過情感分析可以從大量的文字內容中發掘出具價值的知識,可得到可觀的商業、政治、經濟、情報、國安等利益,為企業或個人提供意見或喜好資訊,以支援決策者進行最佳化決策。然而社會網路的文字訊息不同於一般文字文件之特性,在進行文本探勘時便具有其差異性與困難度,故如何在社會網路的環境下,發展一套有效能的概念探勘之方法是不可或缺的。
本研究將採用類神經網路中文件分群之方法,即自我組織圖,將不同的訊息與情感概念加以分群,最後利用相似度分析來協助我們找尋訊息與概念間之關聯。針對所發掘之關聯結果,本研究開發適合社會網路文字訊息之探勘技術,以偵測出訊息之情感概念。
With explosive growth of the Internet, the amount of information in text form is growing rapidly and the demand for data analysis is also increasing. We can perform sentiment analysis on a large set of text messages to discover valuable knowledge and obtain enormous benefits in national security, business, politics, economics, , etc, However, text messages from the social networks are rather different from those of traditional text documents. Therefore, it is difficult but essential to develop an effective method of sentiment exploration in social networks.

In this study we use a neural network method for document clustering, namely the self-organizing maps. We first applied self-organizing maps to cluster similar messages and sentiment keywords. We then developed an association discovery process to find the associations between the messages and sentiment keywords. The sentiment of a message is then determined according to such associations. We performed experiments on Twitter messages and obtained promising results.
摘要 I
ABSTRACT II
圖目錄 V
表目錄 VI
一 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
1.4 研究架構 4
二 文獻探討 5
2.1 情感分析 5
2.1.1 語料庫為本方法 5
2.1.2 詞典為本方法 6
2.2 概念偵測 8
2.2.1 k-means 8
2.2.2 K-NN 9
2.2.3 類神經網路分群 9
三 研究方法 11
3.1 文字訊息擷取 12
3.2 文件前置處理 12
3.2.1 斷詞 12
3.2.2 字詞處理 13
3.2.3 訊息文件向量化 14
3.3 文件分群 15
3.3.1 自我組織圖(SOM)訓練 15
3.3.2 標記(labeling) 19
3.4 關鍵字分群 20
3.4.1 關鍵字向量化 20
3.4.2 關鍵字之自我組織圖訓練 21
3.5 情感分析 22
3.5.1 意見探勘 22
3.5.2 關聯發掘 23
四 實驗結果 26
4.1 實驗步驟 26
4.1.1 前置處理 26
4.1.2 分群與標記 28
4.1.3 情感偵測 32
4.2 實驗評估 39
五 結論與分析 43
5.1結論 43
5.2未來研究發展與建議 43
六 參考文獻 45
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