【摘要】：近年來(lái),隨著移動(dòng)互聯(lián)網(wǎng)的興起,微博類在線社交應(yīng)用得到了快速發(fā)展,微博所具有的開(kāi)放性高、互動(dòng)性強(qiáng)和信息傳播迅速等特點(diǎn)使得它迅速演變成了互聯(lián)網(wǎng)輿情的主要策源地。Twitter和新浪微博作為目前全球使用人數(shù)最多,傳播面最廣的兩大微博系統(tǒng),在輿情分析中占據(jù)著重要的地位。本文面向微博輿情分析的需要,從微博用戶的自身屬性、聯(lián)系的拓?fù)浣Y(jié)構(gòu)以及博文的內(nèi)容分析三個(gè)方面入手,總結(jié)了顯、隱、強(qiáng)、弱四個(gè)維度對(duì)用戶關(guān)系的影響程度,通過(guò)運(yùn)用關(guān)聯(lián)規(guī)則發(fā)現(xiàn)、圖挖掘和文本分析等技術(shù)手段,來(lái)揭示微博用戶在事件傳播中的社會(huì)特性和隱含關(guān)系。本文從分析Twitter的認(rèn)證模式出發(fā),研究了如何突破Twitter限制從而實(shí)時(shí)高效獲取后臺(tái)數(shù)據(jù),并設(shè)計(jì)了一個(gè)Twitter話題圈子分析模型,基于該模型可以對(duì)用戶圈子以不同屬性指標(biāo)進(jìn)行劃分,并能發(fā)現(xiàn)話題圈子中的關(guān)鍵節(jié)點(diǎn)和挖掘在話題發(fā)展中起主要作用的頻繁用戶關(guān)聯(lián)模式,最后通過(guò)實(shí)驗(yàn)證明了該模型在Twitter平臺(tái)輿情分析中可行且有效。本文使用新浪微博開(kāi)發(fā)平臺(tái)抓取輿情事件的微博數(shù)據(jù),根據(jù)博文和話題所表達(dá)主題相關(guān)性的差異,提出了基于主題相關(guān)性分類的微博話題立場(chǎng)研判方法,可以對(duì)用戶進(jìn)行立場(chǎng)劃分并判斷話題的傳播立場(chǎng)。此外也研究了話題主題詞集和立場(chǎng)研判詞庫(kù)的自動(dòng)構(gòu)建方法,在此基礎(chǔ)上本文設(shè)計(jì)了一個(gè)基于微博話題立場(chǎng)研判的用戶劃分模型,可以用于政府有關(guān)部門(mén)監(jiān)測(cè)互聯(lián)網(wǎng)輿情以及企業(yè)評(píng)估產(chǎn)品市場(chǎng)等方面,具有一定的實(shí)用價(jià)值。本文在最后介紹了面向輿情分析的協(xié)同搜索系統(tǒng)整體架構(gòu),并對(duì)其中人物關(guān)聯(lián)分析模塊的功能做了詳細(xì)介紹。人物關(guān)聯(lián)分析模塊主要解決協(xié)同搜索任務(wù)中的人物推薦排序、話題圈子分析以及話題立場(chǎng)研判等功能,之后對(duì)基于二部圖的人物和事件關(guān)聯(lián)模型做了簡(jiǎn)單介紹,隨著該模型經(jīng)過(guò)數(shù)據(jù)積累和功能擴(kuò)充后,可以不斷完善面向互聯(lián)網(wǎng)輿情分析的實(shí)體關(guān)聯(lián)知識(shí)圖譜,為以后超大規(guī)模數(shù)據(jù)下的輿情分析打下堅(jiān)實(shí)的基礎(chǔ)。
[Abstract]:In recent years, with the rise of the mobile Internet, Weibo's online social applications have been rapidly developed, Weibo has a high degree of openness, The characteristics of strong interaction and rapid information dissemination make it rapidly evolve into the main source of Internet public opinion. Twitter and Sina Weibo, as the two largest Weibo systems with the largest number of users and the widest spread in the world, are currently the two largest users in the world. It occupies an important position in the analysis of public opinion. Facing the needs of Weibo's public opinion analysis, this paper summarizes the influence degree of the four dimensions on the user relationship from three aspects: Weibo user's own attributes, the topological structure of the connection and the content analysis of the blog post, and summarizes the influence degree of the four dimensions to the user relationship, namely, explicit, implicit, strong and weak. By means of association rule discovery, graph mining and text analysis, this paper reveals the social characteristics and implicit relationship of Weibo users in the event propagation. Based on the analysis of the authentication mode of Twitter, this paper studies how to break through the limitation of Twitter to obtain the background data efficiently in real time, and designs a Twitter topic circle analysis model. Based on this model, the user circle can be divided into different attribute indexes. The key nodes in the topic circle and the frequent user association pattern which play a main role in the topic development can be found. Finally, the experiment proves that the model is feasible and effective in the public opinion analysis of Twitter platform. This article uses Sina Weibo development platform to grab Weibo data of public opinion events, according to the difference of topic relevance expressed in blog post and topic, puts forward Weibo topic position research method based on theme correlation classification. The user can divide the position and judge the communication position of the topic. In addition, this paper also studies the automatic construction method of topic thesaurus and position judgment thesaurus. On this basis, this paper designs a user partition model based on Weibo's topic position judgment. It can be used to monitor the public opinion on the Internet and evaluate the market of the products by the relevant government departments, which has certain practical value. At the end of this paper, the architecture of collaborative search system for public opinion analysis is introduced, and the function of character association analysis module is introduced in detail. Personage correlation analysis module mainly deals with the functions of character recommendation sorting, topic circle analysis and topic position analysis in collaborative search tasks, and then gives a brief introduction to the character and event association model based on bipartite graph. After data accumulation and function expansion, the model can continuously improve the entity association knowledge graph for Internet public opinion analysis, and lay a solid foundation for future public opinion analysis under super-large-scale data.
【學(xué)位授予單位】：國(guó)防科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092;TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 丁兆云;賈焰;周斌;韓毅;;微博中基于多關(guān)系網(wǎng)絡(luò)的話題影響力個(gè)體挖掘(英文)[J];中國(guó)通信;2013年01期

2 于海群;劉萬(wàn)軍;邱云飛;;基于用戶話題偏好的社會(huì)網(wǎng)絡(luò)二級(jí)人脈推薦[J];計(jì)算機(jī)應(yīng)用;2012年05期

3 李軍;陳震;黃霽崴;;微博影響力評(píng)價(jià)研究[J];信息網(wǎng)絡(luò)安全;2012年03期

4 郭瑞;鐘寧;李文斌;;基于圖熵的社會(huì)網(wǎng)絡(luò)演化分析[J];模式識(shí)別與人工智能;2009年03期

5 楊博;劉大有;金弟;馬海賓;;復(fù)雜網(wǎng)絡(luò)聚類方法[J];軟件學(xué)報(bào);2009年01期

6 劉件;魏程;;中文分詞算法研究[J];微計(jì)算機(jī)應(yīng)用;2008年08期

相關(guān)博士學(xué)位論文 前1條

1 丁兆云;面向微博輿情的影響力分析關(guān)鍵技術(shù)研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2013年

，

本文編號(hào)：2435796