本文選題：情感分類 + 社交網(wǎng)絡(luò)　； 參考：《北京郵電大學(xué)》2015年碩士論文

【摘要】：近年來,微博成為了人們非常重要的在線網(wǎng)絡(luò)活動(dòng)平臺(tái)。很多熱點(diǎn)事件發(fā)生后用戶能夠在微博上獲取相關(guān)消息,并且通過微博平臺(tái)提供的發(fā)布、轉(zhuǎn)發(fā)、評(píng)論等功能十分方便的參與到熱點(diǎn)事件的討論與傳播過程中。微博平臺(tái)開放的機(jī)制使得用戶可以在微博上對(duì)不同主題熱點(diǎn)事件發(fā)表觀點(diǎn)、表達(dá)情感傾向。對(duì)熱點(diǎn)事件相關(guān)微博數(shù)據(jù)進(jìn)行情感分析,可以從中獲取大眾對(duì)具體事件的整體情感狀態(tài)和情感傳播轉(zhuǎn)換情況,這些信息對(duì)輿情分析、營銷效果評(píng)估等實(shí)際應(yīng)用有重要的指導(dǎo)意義。 本文對(duì)中文微博文本的短文本性和新穎性進(jìn)行了分析,針對(duì)這類特性抽取情感學(xué)習(xí)的特征,構(gòu)建了適合中文微博情感傾向分類的模型,并針對(duì)微博情感傾向的正負(fù)極性和中性的不同外延,提出并對(duì)比了一步分類和二步分類策略。在建立的情感分類模型的基礎(chǔ)上,利用微博熱點(diǎn)事件數(shù)據(jù)集對(duì)社會(huì)、娛樂、體育三類主題的典型熱點(diǎn)事件在微博傳播的激烈程度、參與度等方面的情感特性進(jìn)行了分析。在此基礎(chǔ)上,本文進(jìn)一步通過典型熱點(diǎn)事件傳播數(shù)據(jù)集,分析了情感傳播的動(dòng)態(tài)過程和傳播過程中的情感轉(zhuǎn)變模式,描述了情感轉(zhuǎn)換統(tǒng)一概率框架,并且基于條件隨機(jī)場(chǎng)(CRF)構(gòu)建情感轉(zhuǎn)換預(yù)測(cè)模型,能夠?qū)κ录⒉﹤鞑ブ械那楦袀鞑マD(zhuǎn)換進(jìn)行預(yù)測(cè)。 實(shí)驗(yàn)表明,本文構(gòu)建的基于中文微博的情感分類模型對(duì)微博進(jìn)行正向-中性一負(fù)向三種極性的分類,使用一步三分類策略的F1-值為74.9%,而二步分類策略的預(yù)測(cè)效果達(dá)到了82.4%,情感分類的效果較為理想,且驗(yàn)證了二步分類策略的有效性。情感動(dòng)態(tài)傳播中的基于條件隨機(jī)場(chǎng)情感轉(zhuǎn)換預(yù)測(cè)模型對(duì)情感轉(zhuǎn)換的預(yù)測(cè)效果F1-值為60.2%,相較基準(zhǔn)支持向量機(jī)(SVM)模型的效果提高3.7%,能夠較好刻畫情感轉(zhuǎn)換傳播網(wǎng)絡(luò),對(duì)于事件傳播中情感的轉(zhuǎn)換有一定的預(yù)測(cè)能力。
[Abstract]:In recent years, Weibo has become a very important online network platform. After a lot of hot events happen, users can get relevant information on Weibo, and participate in the discussion and propagation of hot events conveniently through the functions of publishing, forwarding, commenting and so on provided by Weibo platform. The open mechanism of Weibo platform enables users to express their opinions and emotional tendencies on Weibo on different topics and hot events. Through emotional analysis of Weibo data related to hot events, we can get the overall emotional state of the public on specific events and the situation of emotional communication conversion, which is the analysis of public opinion. Marketing effect evaluation and other practical applications have important guiding significance. This paper analyzes the short text nature and novelty of Chinese Weibo texts, extracts the characteristics of emotional learning from these features, and constructs a model suitable for the classification of Chinese Weibo affective tendencies. Aiming at the positive and negative pole and neutral extension of Weibo's affective tendency, the one-step classification and two-step classification strategy are proposed and compared. On the basis of the emotion classification model established, the emotional characteristics of the typical hot events of social, entertainment and sports topics in Weibo propagation and participation were analyzed by using the Weibo hot event data set. On this basis, this paper further analyzes the dynamic process and the mode of emotional transformation in the process of emotional communication through the typical hot event propagation data set, and describes the unified probabilistic framework of emotional transformation. Based on conditional Random Field (CRF), a prediction model of affective transformation is constructed, which can predict the emotional transition in event Weibo propagation. The experiment shows that the emotion classification model based on Chinese Weibo is used to classify Weibo with three polarities: forward, neutral and negative. The F1- value of one-step and three-step classification strategy is 74.9, while the prediction effect of two-step classification strategy is 82.4. The effect of emotion classification is satisfactory, and the validity of two-step classification strategy is verified. The prediction effect of conditional random field emotion transformation prediction model in dynamic emotion propagation is 60.20.The result is 3.7g higher than that of SVM model, which can depict affective transformation communication network. There is a certain ability to predict the change of emotion in event communication.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.1

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本文編號(hào)：2053237