本文選題：協(xié)程 + 網(wǎng)絡(luò)輿情��； 參考：《湖南科技大學(xué)》2014年碩士論文

【摘要】：近幾年由于網(wǎng)絡(luò)的發(fā)展，各種信息平臺(tái)如天涯論壇，微博等的出現(xiàn)，加快了以往信息的傳播速度。網(wǎng)絡(luò)中的各種不同觀點(diǎn)借由各種渠道開始迅速傳播，這在方便溝通同時(shí)，亦易造成謠言、社會(huì)不良矛盾的擴(kuò)散，甚至對(duì)于普通的事件進(jìn)行添油加醋從而造成惡劣影響。有介于此，如何對(duì)于網(wǎng)絡(luò)中的不良信息進(jìn)行有效的控制，對(duì)正確的輿論進(jìn)行有效的引導(dǎo)和擴(kuò)散是一個(gè)急需解決的問(wèn)題。 由于網(wǎng)絡(luò)中的信息是以文本表達(dá)，對(duì)于輿情分析的第一步需要對(duì)于文本信息進(jìn)行數(shù)值建模，從而抽取出其中蘊(yùn)含的輿情信息及相互之間的關(guān)系；其次需要對(duì)于用戶進(jìn)行建模，根據(jù)用戶之間的交互來(lái)進(jìn)行親密度建模；最后通過(guò)演化模型對(duì)事件的發(fā)展進(jìn)行預(yù)測(cè)和分析。 有限信任模型考慮基本單位之間的交互關(guān)系，研究其親密度、交互規(guī)則、演化規(guī)則、交互閾值等對(duì)于群體未來(lái)發(fā)展的影響。有限信任模型最初在統(tǒng)計(jì)物理方面顯示出其優(yōu)勢(shì)，之后學(xué)者將其引入輿情演化的研究中，獲得了比較好的效果，經(jīng)過(guò)多年的研究，形成了幾個(gè)比較典型的模型。Hegselmann-Krause模型（H-K模型）是其中的佼佼者，目前主要在仿真中取得了比較好的效果，但是在真實(shí)網(wǎng)絡(luò)中，如何對(duì)于親密度建模、交互輿情設(shè)定等，目前已有的研究還比較少。 針對(duì)這些問(wèn)題，本論文主要開展的工作如下： 1）利用基于協(xié)程的分布式爬蟲框架爬取天涯數(shù)據(jù)，并對(duì)其進(jìn)行數(shù)據(jù)建模及分析。首先介紹了協(xié)程的機(jī)制并實(shí)現(xiàn)了一個(gè)基于協(xié)程的網(wǎng)絡(luò)爬蟲框架，并詳細(xì)介紹了在具體應(yīng)用中的數(shù)據(jù)更新及信息去噪機(jī)制。通過(guò)對(duì)用戶社區(qū)結(jié)構(gòu)的分析，基于用戶活躍度來(lái)對(duì)用戶進(jìn)行分類，并基于回復(fù)關(guān)系來(lái)構(gòu)建活躍用戶社區(qū)，最后利用PageRank來(lái)對(duì)用戶進(jìn)行影響力建模。通過(guò)查詢擴(kuò)展對(duì)論壇建立信息分布模型，通過(guò)對(duì)于事件抽取關(guān)鍵詞，對(duì)其進(jìn)行查詢擴(kuò)展，最終通過(guò)對(duì)于詞頻進(jìn)行統(tǒng)計(jì)，構(gòu)建信息輿論模型。 2）通過(guò)利用H-K模型的演化規(guī)則，基于粒子群的歷史擬合方法對(duì)H-K模型的參數(shù)進(jìn)行調(diào)優(yōu)來(lái)對(duì)輿情演化進(jìn)行預(yù)測(cè)。介紹了Sznajd模型與H-K模型的演化規(guī)則，并對(duì)粒子群算法進(jìn)行了介紹，利用基于粒子群的網(wǎng)絡(luò)擬合方法對(duì)歷史輿情數(shù)據(jù)進(jìn)行分析，通過(guò)基于粒子群方法的擬合來(lái)獲取歷史擬合參數(shù)，并利用演化數(shù)據(jù)進(jìn)行修正從而獲取演化模型，，實(shí)驗(yàn)證明采用歷史擬合方法比利用固定值的方法能夠獲得更高的歷史吻合率。最后通過(guò)實(shí)例分析來(lái)對(duì)我們方法進(jìn)行介紹。 3）通過(guò)對(duì)于天卓輿情系統(tǒng)的設(shè)計(jì)分析，對(duì)數(shù)據(jù)庫(kù)設(shè)計(jì)、架構(gòu)設(shè)計(jì)進(jìn)行了分析和并對(duì)相應(yīng)輿情數(shù)據(jù)采集模塊、輿情數(shù)值建模模塊、輿情演化模塊和輿情展示模塊具體實(shí)現(xiàn)進(jìn)行了分析。
[Abstract]:In recent years, due to the development of the network, the emergence of various information platforms such as Tianya Forum and Weibo has accelerated the speed of information dissemination in the past. All kinds of different viewpoints in the network begin to spread rapidly through various channels, which is easy to communicate, but also easy to cause rumors, the spread of bad social contradictions, and even to add oil to ordinary events, resulting in adverse effects. Therefore, how to effectively control the bad information in the network and guide and spread the correct public opinion is an urgent problem. Because the information in the network is expressed in the text, the first step of the analysis of public opinion needs to carry on the numerical modeling to the text information, so as to extract the public opinion information contained therein and the relationship between them. Secondly, it needs to model the user. Finally, the evolution model is used to predict and analyze the evolution of the event. The finite trust model considers the interaction between basic units, and studies the effects of affinity, interaction rules, evolution rules and interaction threshold on the future development of the population. The limited trust model initially showed its advantages in statistical physics, then the scholars introduced it into the research of the evolution of public opinion, and obtained a better result. After many years of research, The Hegselmann-Krause model (H-K model) is one of the best. At present, it has achieved good results in simulation, but in real networks, how to model affinity, set up interactive public opinion, etc. At present, the existing research is relatively small. In view of these problems, the main work of this paper is as follows: The main contents are as follows: 1) using the distributed crawler framework based on association, crawling the data of the horizon, and modeling and analyzing the data. Firstly, the mechanism of correlation is introduced and a network crawler framework based on correlation is implemented, and the mechanism of data updating and information de-noising in specific applications is introduced in detail. By analyzing the structure of the user community, the user is classified based on the user activity, and the active user community is constructed based on the response relationship. Finally, PageRank is used to model the influence of the user. The information distribution model of the forum is established by query expansion, the key words are extracted for the event, and the query extension is carried out. Finally, the information public opinion model is constructed through the statistics of word frequency. 2) by using the evolution rules of H-K model, the parameters of H-K model are optimized based on the historical fitting method of particle swarm optimization to predict the evolution of public opinion. The evolution rules of Sznajd model and H-K model are introduced, and the particle swarm optimization algorithm is introduced. The historical public opinion data are analyzed by using the network fitting method based on particle swarm optimization, and the historical fitting parameters are obtained by fitting based on particle swarm optimization method. The evolutionary model is obtained by modifying the evolution data. The experimental results show that the historical fitting method can obtain a higher historical coincidence rate than the fixed value method. Finally, through the analysis of examples to introduce our method. 3) through the analysis of the design of Tianzhuo public opinion system, the database design, the architecture design and the corresponding public opinion data collection module, the public opinion numerical modeling module, Public opinion evolution module and public opinion display module are analyzed.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.1;TP393.092

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