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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/97113
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/97113


    Title: 針對臉書粉絲專頁貼文之政治傾向預測
    Predicting Political Affiliation for Posts on Facebook Fan Pages
    Authors: 張哲嘉
    Chang, Che Chia
    Contributors: 徐國偉
    Hsu, Kuo Wei
    張哲嘉
    Chang, Che Chia
    Keywords: 政治傾向
    分類
    臉書
    文字探勘
    political affiliation
    classification
    facebook
    text mining
    Date: 2016
    Issue Date: 2016-06-01 13:53:37 (UTC+8)
    Abstract: 近年來社群媒體興起,尤其以臉書為主。在台灣超過1500萬個臉書用戶,其遍及族群從公眾人物到一般民眾。此外,這類的新興資訊交流平台其實內含許多有意義的資訊,每一則貼文都隱含著每個使用者的情緒以及立場傾向。然而,利用社群媒體來預測選舉與使用者政治傾向已成為目前的趨勢,在台灣各政黨與政治人物紛紛成立粉絲專頁,投入利用網路與社群媒體來打選戰與預測民調。本研究發現此一特性,致力於預測粉絲專頁貼文之政治傾向,收集台灣兩大政黨派國民黨與民進黨之粉絲專頁貼文,建立兩種預測模型分別為以相異字為特徵模型與文字互動特徵模型。利用資料探勘之相關技術,以貼文所含藍綠政黨特徵表現建立分類器,並細部探討與設計多種特徵組合,比較不同特徵組合之預測效果與影響因素以及在預測資料不平衡的情況下是否影響分類結果。最後,研究結果顯示使用文字特徵中黨派典型字與互動特徵值域取對數並搭配KNN分類器效果最佳,其準確度可達0.908,F1-score可達0.827。
    Recently, the social media is becoming more and more popular, especially Facebook. In Taiwan, there are 15 million Facebook users from celebrities to the general public. Receiving information every day from Facebook has become a lifestyle of most people. These new information-exchanging platforms contain lots of meaningful messages including users' emotions and affiliations. Moreover, using the social media data to predict the election result and political affiliation is becoming the current trend in Taiwan. For example, politicians try to win the election and predict the polls by means of Internet and the social media, and every political parties also have their own fan pages. In this thesis, we make an effort to predict the political inclinations of the posts of fan pages, especially for KMT and DPP which are the two largest political parties in Taiwan. We filter the appropriate literal and interactive features. We use the posts of the two parties to predict the political inclinations by constructing the classification models .In the end, we compare the performances of different classifiers .The result shows that the literal and interactive features work the best with KNN classifier, whose accuracy and F1-score are 0.908 and 0.827, respectively.
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    Description: 碩士
    國立政治大學
    資訊科學學系
    103753002
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0103753002
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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