淡江大學機構典藏:Item 987654321/114446
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    Title: 應用文字探勘技術於台北市政府施政滿意度分析
    Other Titles: Analysis of satisfaction to administer of Taipei City Government with text mining techniques
    Authors: 王雅芬;WANG, YA-FEN
    Contributors: 淡江大學統計學系碩士班
    陳景祥;Chen, Ching-Hsiang
    Keywords: 施政滿意度;網路輿情;情感分析;文字探勘;internet public opinion;satisfaction;Sentiment analysis;text mining
    Date: 2017
    Issue Date: 2018-08-03 14:52:51 (UTC+8)
    Abstract: 隨著網際網路的發展與普及,越來越多人在網路上發表想法或意見,形成台灣人民對政治事件、政治人物看法的網路輿情。運用文字探勘方法,我們能夠彙整網路上的文本資料,取出資訊進行輿情分析,從而更了解人民對於當今政府政策相關的意見,幫助執政者調整政策方向或執政方式。
    本研究藉由SO-PMI方法及資訊增益的方法擴充情感詞典,比較
    TF-IDF變數表示法、情感變數表示法以及多變數表示法對文章進行情感分析,結合網路輿情指標,以評估民眾對台北市政府施政的滿意程度。本研究結論為使用多變數表示法和支持向量機進行情感分類結果較好,用議題相近的文本建模能提昇預測準確率,本研究提出的評估滿意度方法可輔助民調,一同評估市民對北市府的滿意程度。
    As new technologies advances, internet become more popular. More and more person states their opinion on internet. In democratic society, people have suffrage and freedom of speech. People always share their opinions about policy on the internet. To know the opinions of people, we must employ a lot of employees to make phone-based poll in the past. Nowadays, we can crawl and download the articles easily on the internet and use the text mining techniques to deal with political issues. Then, we can estimate the sentiment orientations of political articles and show the political orientations of internet users.
    This paper uses semantic orientation from PMI method and information gain method to add sentimental terms in sentimental dictionary. We make comparison between TF-IDF variable, sentimental variable and combined variable models in the classification of sentiment. We also build the model of sentiment analysis and develop an internet public opinion index to estimate the degree of satisfaction to administer of Taipei City Government of Taipei’s citizen.
    With the best accuracy and excellent stability, Support Vector Machine is the best choice for us to do the sentimental classification. If the topic of training data is similar to the topic of the testing data, the testing accuracy will be higher. Do text mining analysis for internet texts is helpful for us to analyze internet public opinions.
    Appears in Collections:[Graduate Institute & Department of Statistics] Thesis

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