本文選題：微博　切入點：情感詞典　出處：《南京郵電大學》2014年碩士論文　論文類型：學位論文

【摘要】：近年來微博已逐漸成為了一種主流的消息發(fā)布渠道。大量網(wǎng)絡(luò)用戶通過微博快速及時的傳遞信息、發(fā)表意見、表達情感。網(wǎng)絡(luò)熱點事件、熱點話題等都可以通過微博快速傳播并可以通過微博迅速收集網(wǎng)絡(luò)民眾對此的看法。正因如此，近年來，針對用戶在微博中所表露的情感進行分析，已成為一個新的研究熱點。 情感分析主要包含三個方向的情感傾向，即積極傾向、中立傾向和消極傾向。常用的情感分析方法包括基于機器學習和基于情感詞典的方法，本文采取基于情感詞典的中文微博情感分析方法。主要工作包括： 利用中文微博的自身特點對微博進行預(yù)處理，采用基于多種傳統(tǒng)方法的組合型分詞方法，針對分詞過程中經(jīng)常出現(xiàn)的交集型歧義提出解決方法，提取出微博中的有效情感元素；對微博中有效情感元素中的情感詞、程度副詞、否定詞、表情符號等進行相應(yīng)的情感分析處理，進而得到整個微博的情感極性和情感強度。 考慮到中文微博中存在網(wǎng)絡(luò)反語的現(xiàn)象，本文基于已得到的微博情感極性和情感強度作進一步的情感分析，提出了基于MMTD（Measure of MediumTruth Degree）的微博情感分析方法，對每條微博作出最終的情感傾向判斷。本文從新浪微博熱點問題中選取了8個微博熱門話題，分別涉及到不同的社會熱點問題，采用本文提出的基于MMTD的分析方法對微博情感傾向性進行分析，，實驗結(jié)果表明本文方法能夠發(fā)現(xiàn)微博中存在的反語，有助于提高微博情感傾向性判斷的準確性。
[Abstract]:In recent years, Weibo has gradually become a mainstream channel for news release. A large number of Internet users pass information, express their opinions, express their feelings, and express their feelings quickly and in a timely manner through Weibo. Hot topics and other topics can be quickly spread through Weibo and online public views on this can be quickly collected through Weibo. Therefore, in recent years, the analysis of the emotions revealed by users in Weibo has become a new research hotspot. Affective analysis mainly includes three kinds of affective tendencies, that is, positive tendency, neutral tendency and negative tendency. The commonly used affective analysis methods include machine learning and affective dictionary based methods. This paper adopts the affective dictionary based Chinese Weibo emotion analysis method. The main work includes:. Taking advantage of Weibo's own characteristics, we pretreat Weibo, adopt combinatorial word segmentation method based on many traditional methods, and propose solutions to the overlapping ambiguity that often occurs in the process of word segmentation. The effective emotional elements in Weibo and the affective words, degree adverbs, negative words, emoji and emoticons in the effective emotional elements of Weibo were analyzed and processed accordingly, and then the emotional polarity and intensity of the whole Weibo were obtained. Considering the phenomenon of network irony in Chinese Weibo, this paper gives a further emotional analysis based on the obtained affective polarity and intensity of Weibo, and puts forward an affective analysis method based on MMTD(Measure of MediumTruth grave. Make the final judgment on each Weibo's emotional tendency. This paper selects 8 hot topics from the hot topics of Sina Weibo, which involve different social hot issues. The analysis method based on MMTD proposed in this paper is used to analyze Weibo's emotional tendency. The experimental results show that the method in this paper can find the irony existing in Weibo and help to improve the accuracy of Weibo's emotional tendentiousness judgment.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP391.1;TP393.092

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