本文選題：網(wǎng)絡(luò)輿情　切入點(diǎn)：垃圾評(píng)論識(shí)別　出處：《中國(guó)民航大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著我國(guó)民航業(yè)的高速發(fā)展,大眾對(duì)民航行業(yè)的關(guān)注度越來越高。微博、論壇等新媒體使民航輿情事件被高度關(guān)注。網(wǎng)民會(huì)借助這些平臺(tái)發(fā)表自己關(guān)于民航事件的評(píng)論,但網(wǎng)民產(chǎn)生的評(píng)論中存在與話題無關(guān),甚至是虛假的垃圾評(píng)論,所以在對(duì)民航事件分析前,首先需處理垃圾評(píng)論。此外,當(dāng)前網(wǎng)民評(píng)論的情感傾向會(huì)對(duì)未來網(wǎng)民對(duì)同一事件的態(tài)度產(chǎn)生影響,因此準(zhǔn)確客觀的對(duì)評(píng)論進(jìn)行情感分析并對(duì)發(fā)展趨勢(shì)做出預(yù)測(cè),對(duì)評(píng)估民航事件輿情的發(fā)展趨勢(shì)并提前進(jìn)行應(yīng)對(duì),是非常重要的。針對(duì)垃圾評(píng)論的識(shí)別和過濾,本文界定了評(píng)論是否重復(fù)出現(xiàn)、評(píng)論中政府部門出現(xiàn)次數(shù)等六個(gè)指標(biāo)作為識(shí)別垃圾評(píng)論的特征。采用信息增益算法對(duì)特征進(jìn)行權(quán)重計(jì)算,并利用粒子群優(yōu)化的支持向量機(jī)模型(PSO-SVM)進(jìn)行垃圾評(píng)論的識(shí)別和過濾。因獲取預(yù)測(cè)指標(biāo)是網(wǎng)絡(luò)輿情情感趨勢(shì)預(yù)測(cè)的前提,本文提出了不同于以往的單純熱度指標(biāo)(例如,關(guān)注度、評(píng)論回復(fù)數(shù)、轉(zhuǎn)發(fā)數(shù)等)的評(píng)論情感傾向性值時(shí)間序列的預(yù)測(cè)指標(biāo)。又因情感傾向性值呈現(xiàn)非線性、隨機(jī)性的特征,本文采用相關(guān)向量機(jī)模型進(jìn)行趨勢(shì)預(yù)測(cè)來提高精度。本文設(shè)計(jì)了實(shí)驗(yàn),對(duì)文中研究成果做了分析和驗(yàn)證。針對(duì)識(shí)別和過濾垃圾評(píng)論的問題,實(shí)驗(yàn)分析了界定垃圾評(píng)論的特征數(shù)量和不同特征對(duì)垃圾評(píng)論識(shí)別的影響,實(shí)驗(yàn)結(jié)果說明了選擇合適的特征對(duì)于垃圾評(píng)論識(shí)別的重要性。對(duì)于情感趨勢(shì)預(yù)測(cè),本文將相關(guān)向量機(jī)模型、Elman神經(jīng)網(wǎng)絡(luò)及BP神經(jīng)網(wǎng)絡(luò)模型各自的預(yù)測(cè)結(jié)果進(jìn)行了對(duì)比實(shí)驗(yàn)。利用平均絕對(duì)誤差(MAE)和均方根誤差(RMSE)評(píng)價(jià)預(yù)測(cè)的準(zhǔn)確性。通過對(duì)比實(shí)驗(yàn)說明,相關(guān)向量機(jī)的預(yù)測(cè)性能優(yōu)于其他兩種模型并能更為準(zhǔn)確的反映網(wǎng)民對(duì)輿情事件的情感趨勢(shì)。故本文對(duì)民航輿情分析中的垃圾評(píng)論識(shí)別和情感趨勢(shì)預(yù)測(cè)的研究是有意義的。
[Abstract]:With the rapid development of the civil aviation industry in China, the public is paying more and more attention to the civil aviation industry. New media such as Weibo, forum and other new media have made civil aviation public opinion events highly concerned. Netizens will use these platforms to make their own comments on civil aviation affairs. However, there are comments generated by Internet users that have nothing to do with the topic, or even false spam comments. Therefore, before analyzing the civil aviation incident, we should first deal with the garbage comments. In addition, The emotional tendency of current netizens' comments will have an impact on the attitude of future netizens to the same event, so accurately and objectively carry out the emotional analysis of the comments and make a prediction of the development trend. It is very important to assess the development trend of public opinion on civil aviation incidents and to deal with it in advance. In view of the identification and filtering of garbage comments, this paper defines whether the comments are repeated. Six indexes, such as the number of government departments appearing in the comments, are used to identify the spam comments. The information gain algorithm is used to calculate the weight of the features. The support vector machine model based on particle swarm optimization (PSO) is used to identify and filter garbage comments. Since obtaining prediction index is the premise of prediction of sentiment trend of network public opinion, this paper proposes a simple heat index (for example, concern degree), which is different from previous ones. The prediction index of the time series of the emotional tendency value of the comment, the response number of comment, the number of retweets, etc., and because of the nonlinear and random characteristics of the emotional tendency value, In this paper, the correlation vector machine model is used to predict the trend to improve the accuracy. Experiments are designed, and the research results are analyzed and verified. The experimental results show the importance of choosing suitable features for garbage comment recognition, and the prediction of emotion trend. In this paper, the correlation vector machine model Elman neural network and the BP neural network model are compared. The accuracy of the prediction is evaluated by using the mean absolute error (mae) and the root mean square error (RMSE). The prediction performance of correlation vector machine is better than the other two models and can more accurately reflect the emotional trend of Internet users' public opinion events. So this paper is meaningful to the garbage comment identification and emotional trend prediction in civil aviation public opinion analysis.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.1

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本文編號(hào)：1610019