本文選題：推特 + 社團(tuán)檢測(cè)��； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展,社交媒體在人們生活中的應(yīng)用越來越多樣化。而推特(Twitter)作為社交媒體中的佼佼者,已經(jīng)成為近年來最流行的社交媒體應(yīng)用之一。而社交媒體在政治事件中的影響也在與日俱增。2012美國(guó)總統(tǒng)奧巴馬連任、英國(guó)脫歐公投等一系列政治事件的背景中都出現(xiàn)了推特的身影。推特在事件傳播、反映民眾的政治傾向上有逐步取代傳統(tǒng)民意調(diào)查的趨勢(shì)。目前在社交媒體上的政治傾向研究主要針對(duì)文本中的特定信息來進(jìn)行分析,例如推文中的標(biāo)簽(hashtag)、提到(@)等。由于社交媒體數(shù)據(jù)不具有正式性,所以政治傾向分析的結(jié)果不夠精確。同時(shí)對(duì)于社交媒體中的大選選情預(yù)測(cè)并沒有特別完善的流程和方案。所以,本文通過圖挖掘的方法,對(duì)社交媒體中的政治類事件進(jìn)行分析研究。針對(duì)2016美國(guó)總統(tǒng)大選選情預(yù)測(cè)的問題,提出并設(shè)計(jì)了大選選情預(yù)測(cè)模型。本文的主要工作和創(chuàng)新點(diǎn)概括如下:(1)在推特?cái)?shù)據(jù)上進(jìn)行政治傾向情感分析和大選相關(guān)事件檢測(cè)。在情感分析上,針對(duì)推特信息簡(jiǎn)短、非正式、缺乏補(bǔ)充信息的特點(diǎn),本文采用了基于字典的情感分析方法,對(duì)推文的政治傾向性進(jìn)行判斷。同時(shí)對(duì)于情感分析中的反語(yǔ)鑒別難點(diǎn),通過推文的表情符以及用戶的歷史推文來提升情感分析的結(jié)果。在事件檢測(cè)中,針對(duì)美國(guó)總統(tǒng)大選的背景,本文采用了多次聚類、同義詞拓展,關(guān)鍵詞權(quán)重提升等方法,來對(duì)碎片事件進(jìn)行整合。該方法提升了大選相關(guān)事件檢測(cè)的性能。(2)利用圖挖掘的方法來對(duì)推特?cái)?shù)據(jù)進(jìn)行研究分析。由于推特?cái)?shù)據(jù)源上有多種多樣的信息,例如用戶,推文,圖片,視頻等等。而用戶的點(diǎn)贊,轉(zhuǎn)發(fā)和評(píng)論行為,往往也會(huì)表露出用戶的政治傾向。所以,本文采用復(fù)雜網(wǎng)絡(luò)分析方法,將不同類型的社交媒體數(shù)據(jù)投影到復(fù)雜網(wǎng)絡(luò)中,再根據(jù)實(shí)際需求,對(duì)社交媒體復(fù)雜網(wǎng)絡(luò)進(jìn)行分析。在大選預(yù)測(cè)結(jié)果中發(fā)現(xiàn)支持總統(tǒng)候選人的用戶社團(tuán)。這種分析方法不僅僅適用于大選選情預(yù)測(cè),還可以應(yīng)用于社交媒體的輿論導(dǎo)向分析、用戶影響力等方面。本文使用了真實(shí)社交媒體數(shù)據(jù),分別對(duì)情感分析方法,事件檢測(cè)模型和大選預(yù)測(cè)模型進(jìn)行實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果顯示,本文提出的大選預(yù)測(cè)模型可以對(duì)大選選情進(jìn)行正確預(yù)測(cè)。
[Abstract]:With the development of Internet, the application of social media in people's life is more and more diversified. Twitter, a leader in social media, has become one of the most popular social media applications in recent years. Social media influence in political events is also growing. 2012 U.S. President Barack Obama re-elected, the British Brexit referendum and other political events in the background of Twitter. Twitter's spread of events reflects a gradual displacing of traditional opinion polls from popular political tendencies. The current research on political orientation on social media is mainly focused on the analysis of specific information in the text, such as the tag (hashtag), mentioned in Twitter (@), and so on. Because social media data are not formal, the analysis of political tendencies is not accurate enough. At the same time, there is no particularly sound process and program for social media election predictions. Therefore, this paper analyzes the political events in social media by the method of graph mining. Aiming at the problem of presidential election prediction in 2016, this paper puts forward and designs a model of presidential election prediction. The main work and innovations of this paper are summarized as follows: (1) the analysis of political tendency and emotion and the detection of election-related events on Twitter data. In terms of affective analysis, in view of the features of short, informal and lack of supplementary information, this paper adopts a dictionary-based emotional analysis method to judge the political tendency of Twitter. At the same time, for the difficulty of identifying irony in affective analysis, the result of emotional analysis is improved by the emoji of tweets and the historical tweets of users. In the event detection, in view of the background of the American presidential election, this paper uses several methods, such as clustering, synonym extension, keyword weight enhancement, to integrate the debris events. This method improves the performance of election related event detection. (2) the method of graph mining is used to study and analyze Twitter data. Because Twitter data sources have a variety of information, such as users, tweets, pictures, videos and so on. Users' likes, retweets and comments often show their political inclination. Therefore, this paper uses the method of complex network analysis, projects different types of social media data to complex network, and then analyzes the complex social media network according to the actual demand. In the election forecast results found in support of the presidential candidate user community. This analysis method not only applies to election prediction, but also can be applied to social media opinion oriented analysis, user influence and so on. In this paper, real social media data are used to test affective analysis, event detection model and general election prediction model. The experimental results show that the proposed general election prediction model can correctly predict the election results.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5;TP391.1

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