【摘要】：近年來,互聯(lián)網(wǎng)的迅猛發(fā)展促進(jìn)了信息技術(shù)與網(wǎng)絡(luò)通信技術(shù)的發(fā)展。社會(huì)生活的高度信息化,使網(wǎng)絡(luò)承載了蘊(yùn)含價(jià)值的數(shù)據(jù),擁有海量用戶的社會(huì)化網(wǎng)絡(luò)媒體,已經(jīng)被組織和個(gè)人廣泛地用來輔助決策。在線金融論壇上存在巨大的用戶群與潛在的商機(jī),使虛假意見和垃圾信息被廣泛地制造和傳播,該類危害的源頭即惡意用戶群組。針對(duì)以上問題,我們利用網(wǎng)頁信息提取、數(shù)據(jù)存儲(chǔ)、情感分析、網(wǎng)絡(luò)關(guān)系建模、重疊社區(qū)檢測(cè)等技術(shù),來采集在線金融論壇用戶行為數(shù)據(jù)、構(gòu)建用戶關(guān)系網(wǎng)絡(luò)、對(duì)用戶關(guān)系網(wǎng)絡(luò)進(jìn)行社區(qū)劃分、檢測(cè)惡意用戶群組并評(píng)價(jià)檢測(cè)結(jié)果。本文的主要工作如下:1.通過對(duì)在線金融論壇網(wǎng)站頁面的研究,分析論壇用戶行為,利用網(wǎng)頁信息抽取技術(shù)采集論壇頁面信息,匹配實(shí)驗(yàn)所需的用戶行為數(shù)據(jù),并存儲(chǔ)到本地關(guān)系型數(shù)據(jù)庫My SQL中。2.基于機(jī)器學(xué)習(xí),對(duì)訓(xùn)練集進(jìn)行分詞、特征選取,選擇合適的情感分類器,對(duì)用戶評(píng)論內(nèi)容的情感進(jìn)行分類預(yù)測(cè),依據(jù)預(yù)測(cè)分類結(jié)果,構(gòu)建用戶行為網(wǎng)絡(luò)關(guān)系模型,并描述用戶相似情感網(wǎng)絡(luò)的相關(guān)全局性統(tǒng)計(jì)特征,得出相似情感網(wǎng)絡(luò)既滿足“小世界”特性,也滿足無尺度特性。3.考慮到節(jié)點(diǎn)屬性對(duì)數(shù)據(jù)結(jié)構(gòu)的影響,結(jié)合節(jié)點(diǎn)拓?fù)浣Y(jié)構(gòu)和節(jié)點(diǎn)屬性信息,提出一種基于節(jié)點(diǎn)拓?fù)浣Y(jié)構(gòu)和節(jié)點(diǎn)屬性的重疊社區(qū)檢測(cè)算法,對(duì)在線金融論壇用戶關(guān)系網(wǎng)絡(luò)和斯坦福大學(xué)的三個(gè)社交網(wǎng)絡(luò)數(shù)據(jù)集進(jìn)行重疊社區(qū)檢測(cè),并與常見的社區(qū)檢測(cè)算法作比較,驗(yàn)證了本文提出算法的可行性與有效性。4.提出相應(yīng)的社區(qū)檢測(cè)的外部指標(biāo),綜合這些外部指標(biāo)檢測(cè)股票論壇中的惡意用戶群組,并結(jié)合具體案例分析。
[Abstract]:In recent years, the rapid development of the Internet has promoted the development of information technology and network communication technology. With the high degree of information in social life, the network carries valuable data, and the social network media, which has a large number of users, has been widely used by organizations and individuals to assist decision-making. There are huge user groups and potential business opportunities in online financial forums, so that false opinions and junk information are widely produced and disseminated, and the source of this kind of harm is malicious user groups. In order to solve the above problems, we use web page information extraction, data storage, emotional analysis, network relationship modeling, overlapping community detection and other technologies to collect online financial forum user behavior data, build user relationship network, divide user relationship network into communities, detect malicious user groups and evaluate the detection results. The main work of this paper is as follows: 1. Through the research of the website page of the online financial forum, this paper analyzes the user behavior of the forum, collects the forum page information by using the web page information extraction technology, matches the user behavior data needed in the experiment, and stores it in the local relational database My SQL. 2. Based on machine learning, word segmentation, feature selection, selection of appropriate emotional classifiers, classification and prediction of the emotion of user comment content, according to the prediction classification results, the relationship model of user behavior network is constructed, and the related global statistical characteristics of user similar emotional network are described. it is concluded that the similar emotional network not only satisfies the characteristics of "small world", but also satisfies the characteristics of no scale. Considering the influence of node attributes on data structure, combined with node topology and node attribute information, an overlapping community detection algorithm based on node topology and node attributes is proposed. The overlapping community detection of online financial forum user relationship network and three social network data sets of Stanford University is carried out, and compared with the common community detection algorithms, the feasibility and effectiveness of the proposed algorithm are verified. 4. This paper puts forward the corresponding external indicators of community detection, synthesizes these external indicators to detect malicious user groups in stock forums, and analyzes the specific cases.
【學(xué)位授予單位】：南京財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.092;TP391.1

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本文編號(hào)：2499210