發(fā)布時(shí)間：2018-01-07 00:38

  本文關(guān)鍵詞：網(wǎng)絡(luò)輿情語義識(shí)別的技術(shù)分析及識(shí)別流程構(gòu)建　出處：《吉林大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 網(wǎng)絡(luò)輿情 語義識(shí)別 輿情分析 預(yù)警機(jī)制

【摘要】：隨著現(xiàn)代社會(huì)網(wǎng)絡(luò)化進(jìn)程的加速,網(wǎng)絡(luò)已經(jīng)成為人們表達(dá)個(gè)人意見和觀點(diǎn)的重要渠道。網(wǎng)絡(luò)輿情信息成為輿情信息的一個(gè)關(guān)鍵部分。網(wǎng)絡(luò)輿情和傳統(tǒng)輿情相比,具有數(shù)據(jù)量大、突發(fā)性強(qiáng)、影響范圍廣等特點(diǎn)。在大數(shù)據(jù)環(huán)境背景下,網(wǎng)絡(luò)輿情信息的挖掘較以往的傳統(tǒng)媒體更加困難,并且更加難以規(guī)范。因此,如何利用語義識(shí)別技術(shù),有效地從海量的輿情信息中挖掘出其中的關(guān)鍵因素并指導(dǎo)決策以及突發(fā)事件的處理就成為輿情研究的重要方向。本文通過分析研究網(wǎng)絡(luò)輿情語義識(shí)別的各類技術(shù),重新建立一套網(wǎng)絡(luò)輿情語義識(shí)別的流程。該流程不僅包含網(wǎng)絡(luò)輿情語義識(shí)別的技術(shù)細(xì)節(jié),同時(shí)也建立起一套網(wǎng)絡(luò)輿情突發(fā)事件的評(píng)判標(biāo)準(zhǔn)提供參考。本文所構(gòu)建的網(wǎng)絡(luò)輿情語義識(shí)別的流程包含網(wǎng)絡(luò)輿情信息采集模塊、網(wǎng)絡(luò)輿情預(yù)處理模塊、網(wǎng)絡(luò)輿情話題識(shí)別模塊以及網(wǎng)絡(luò)輿情反饋模塊等四組子模塊,前三個(gè)模塊的主要工作是對(duì)網(wǎng)絡(luò)輿情進(jìn)行識(shí)別分析,從浩如煙海的網(wǎng)絡(luò)信息中將網(wǎng)絡(luò)輿情信息加以提取,所以這三個(gè)子模塊是輿情預(yù)警與疏導(dǎo)的基礎(chǔ);而網(wǎng)絡(luò)輿情反饋模塊則是基于上述三個(gè)子模塊的識(shí)別結(jié)果對(duì)網(wǎng)絡(luò)輿情態(tài)勢進(jìn)行系統(tǒng)的分析,并針對(duì)輿情突發(fā)事件的不同爆發(fā)狀態(tài)提出相應(yīng)的疏導(dǎo)策略。本文針對(duì)當(dāng)前網(wǎng)絡(luò)輿情爆發(fā)的主要陣地微博平臺(tái)提出了實(shí)證研究,選取了當(dāng)前的輿論熱點(diǎn)話題進(jìn)行語義識(shí)別分析,并預(yù)警突發(fā)事件,提出疏導(dǎo)建議。本文的創(chuàng)新點(diǎn)主要有:將網(wǎng)絡(luò)輿情信息語義識(shí)別技術(shù)進(jìn)行歸類,比較分析各種語義識(shí)別技術(shù)找到其各方面的特征,比較分析的方面主要有:信息處理的精度比較、人工參與程度比較、特征庫比較、多媒體信息的處理能力比較、深層次語義信息的挖掘比較、技術(shù)復(fù)雜程度比較以及通用性、適應(yīng)性的比較。對(duì)網(wǎng)絡(luò)輿情信息的語義識(shí)別技術(shù)進(jìn)行研究,并根據(jù)網(wǎng)絡(luò)輿情的特點(diǎn),提出一個(gè)網(wǎng)絡(luò)輿情語義識(shí)別的技術(shù)流程方案。整個(gè)流程分為信息采集、預(yù)處理、話題分析、輿情反饋等幾個(gè)步驟。在網(wǎng)絡(luò)輿情信息的采集過程中,本文采用通用網(wǎng)絡(luò)爬蟲技術(shù)對(duì)網(wǎng)絡(luò)信息進(jìn)行爬取;在獲取了基礎(chǔ)的數(shù)據(jù)集合之后,需要對(duì)信息集合進(jìn)行預(yù)處理,將文本進(jìn)行分析處理并去除文本中的停用詞,對(duì)文本信息的特征進(jìn)行抽取;隨后,對(duì)輿情信息的文本集合進(jìn)行聚類分析,使用文本向量模型對(duì)文本信息金星表示,采用K-means聚類算法對(duì)文本信息進(jìn)行聚類,挖掘信息話題;對(duì)信息的情感傾向性進(jìn)行分提取,對(duì)其情感傾向的強(qiáng)弱進(jìn)行排序,得出輿情語義識(shí)別的結(jié)果。
[Abstract]:With the acceleration of modern social network, the network has become an important channel for people to express personal opinions and views. The network public opinion information has become a key part of public opinion information. Compared with traditional network public opinion and public opinion, with a large amount of data, strong burst characteristics, affecting a wide range. In the big data environment, mining network public opinion information than the previous traditional media is more and more difficult, and more difficult to regulate. Therefore, how to use semantic recognition technology, effectively from massive public opinion information to dig out the key factors and guide the decision-making and handling of emergencies has become an important direction of public opinion research. Through the analysis of network public opinion research in semantic recognition all kinds of technology, to establish a set of network public opinion semantic recognition process. The process includes not only the technical details of the semantic recognition of network public opinion, also built Set up a set of reference network publicsentiment emergency evaluation standard. The network public opinion semantic recognition process includes information collection module, preprocessing module of network public opinion, the network public opinion, the network public opinion topic identification module and network public opinion feedback module of four sub modules, the main work of the first three modules is analyze public opinion on the network, to extract information from the network, the network public opinion information multitude, so the three sub module is the basis of public opinion warning and persuasion; and the network public opinion feedback module is the identification of the three sub modules based on the results of the online public opinion situation are systematically analyzed, and according to the different publicsentiment emergency outbreak status of leading the corresponding strategies. Aiming at the main position of the current network outbreak of public opinion presents an empirical study on the micro-blog platform, select the Public opinion topic semantic recognition analysis, early warning and emergency, put forward guidance suggestions. The main innovations of this paper are: the network public opinion information semantic recognition technology classification, comparative analysis of various semantic recognition technology to find the characteristics of the various aspects of the comparative analysis are: comparison of the accuracy of information processing, artificial participation degree feature library comparison, comparison, processing of multimedia information, mining deep semantic information, technical complexity and versatility, adaptability. Research on semantic recognition technology of network public opinion information, and according to the characteristics of network public opinion, put forward a network public opinion semantic recognition technology. The whole process flow scheme is divided into information collection, preprocessing, topic analysis, public opinion feedback and other steps. In the process of collecting information in network public opinion, the general network Network crawler technology for network information crawling; after obtaining the basic data set, the need for information collection pretreatment, text analysis and text processing to remove the stop words, to extract features of text information; then, the collection of information for cluster analysis, said the text use the text information of Venus vector model, K-means algorithm is used to cluster the text information mining, information extraction of topic; emotional tendency of information, on the strength of the emotional inclinations that sort of public opinion semantic recognition results.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：G254

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