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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/87732

    Title: 部落格文章情感分析之研究
    Other Titles: A study of sentimental analysis in blog articles
    Authors: 簡之文;Chien, Chih-Wen
    Contributors: 淡江大學資訊管理學系碩士班
    Keywords: 情感分析;支援向量機;知網;主觀情緒;sentimental analysis;SVM;Hownet;subjective emotion
    Date: 2012
    Issue Date: 2013-04-13 11:36:49 (UTC+8)
    Abstract: 如何在龐大的網路社群文章中,有效且快速的擷取所要的情感評論,是情感分析(Sentiment Analysis)重要的基礎工作,本研究選擇在語句層級探討,分析文章中的主觀情緒評論語句。嘗試找出主觀情緒語句與非主觀情緒語句的判斷模式。本研究採用系統發展研究法,使用SVM(Support Vector Machine)工具將主觀情緒語句進行訓練與測試,實驗中再將SVM產出的分類與實際分類做比對,計算其準確率做為系統驗證的依據。
    How to capture the emotional opinion efficiently and quickly in a lot of network community articles is an important basic work for Sentiment Analysis. This study is discussed in the sentence-level analysis of the subjective emotional comment sentences in the article, and tries to find out a judgment model of subjective emotional sentences and non-subjective emotional sentences. This study adopts Systems Development Methodology, and it uses SVM tool to train and test the subjective emotional sentences. In this experiment, it compares the classification of SVM with the actual classification and it calculates the accuracy as the basis for system verification.
    On the basis of the classification of SVM, this study found if the tolerance value of the distance between attribute word and opinion phrase is set to 3, it will significantly improve the accuracy of distance classification and reduce the computing cost of distance comparison, and then the classification feature of the attribute words is better than negative words and opinion words in recognizing the distance relationship between attribute word and opinion phrase.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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