本文選題：深度學(xué)習(xí) + 網(wǎng)絡(luò)輿情　； 參考：《湘潭大學(xué)》2017年碩士論文

【摘要】：隨著中國互聯(lián)網(wǎng)的爆發(fā)式發(fā)展,龐大的網(wǎng)絡(luò)社交群體產(chǎn)生了海量的網(wǎng)絡(luò)輿情,其情感極其容易被傳播和感染。而如何通過龐雜的輿情信息來捕捉分析民眾的情感趨勢,引導(dǎo)正向的輿情傳播,保障社會(huì)的安定和諧,是一項(xiàng)極為重要的研究課題。中文文本情感分類作為輿情分析的核心環(huán)節(jié)之一,備受學(xué)者關(guān)注和研究。由于中文文本數(shù)據(jù)具有語義多元、語法特殊性、隱寓性表達(dá)等諸多特點(diǎn),加之當(dāng)前中文文本情感分類方法大多屬于淺層學(xué)習(xí)方法,存在文本表征能力有限、依賴人工抽取樣本特征等缺陷,難以獲得較高的中文文本情感分類準(zhǔn)確率。為此,如何切合中文文本特點(diǎn),進(jìn)一步提升中文文本情感分類的性能是當(dāng)下網(wǎng)絡(luò)輿情分析領(lǐng)域迫切需要研究的內(nèi)容。本文基于深度學(xué)習(xí)方法開展中文文本情感分類研究,并應(yīng)用于網(wǎng)絡(luò)輿情的分析,相關(guān)研究內(nèi)容如下:1.基于主題融合的深度學(xué)習(xí)情感分類。針對傳統(tǒng)深度學(xué)習(xí)情感分類中只采用詞特征的局限性,本文將主題特征與深度學(xué)習(xí)模型相結(jié)合,構(gòu)建兩種主題融合的深度學(xué)習(xí)情感分類模型:TB_LSTM、TCNN。該兩種模型能融合主題特征來獲得優(yōu)質(zhì)的高層文本特征。實(shí)驗(yàn)表明,在二元情感分類中,兩種模型最高分類準(zhǔn)確率分別達(dá)到91.1%和91.9%,比LSTM、CNN、RAE等常用深度學(xué)習(xí)模型的情感分類準(zhǔn)確率平均高出2%左右。2.面向增強(qiáng)特征提取的深度學(xué)習(xí)情感分類。為提升模型的特征提取能力,本文借鑒特征融合思想,將兩種深度學(xué)習(xí)情感分類模型(TB_LSTM和TCNN)提取的高層文本特征進(jìn)行融合,以此增強(qiáng)模型的文本特征提取能力,并構(gòu)建了TB_LSTM+TCNN情感分類模型。實(shí)驗(yàn)表明,在相同實(shí)驗(yàn)數(shù)據(jù)條件下,TB_LSTM+TCNN情感分類模型的二元情感分類準(zhǔn)確率比TB_LSTM和TCNN的分類準(zhǔn)確率高出0.8%-1.6%。3.基于深度學(xué)習(xí)的多維度輿情分析智能化建模。針對中文網(wǎng)絡(luò)輿情分析的全面性和準(zhǔn)確性需求,本文構(gòu)建了基于深度學(xué)習(xí)的多維度輿情智能分析模型,該模型能利用基于增強(qiáng)特征提取深度學(xué)習(xí)情感分類模型實(shí)現(xiàn)精準(zhǔn)的中文文本情感分類,并結(jié)合主題模型和時(shí)間序列模型實(shí)現(xiàn)多維度的情感分類、多維度的情感走勢分析與預(yù)測。4.基于深度學(xué)習(xí)的多維度輿情分析實(shí)證研究。為驗(yàn)證文章提出的深度學(xué)習(xí)多維度輿情分析模型的有效性,論文以“魏則西事件”為實(shí)證案例,基于文章提出的多維度輿情智能分析模型實(shí)現(xiàn)對“魏則西事件”的深度解析,實(shí)現(xiàn)了多維度的輿情情感走勢分析、輿情熱點(diǎn)追蹤、情感演化刻畫,并通過與專家結(jié)論對比證明了該模型的有效性和實(shí)用性。
[Abstract]:With the explosive development of the Internet in China, a large number of online social groups have generated a huge amount of online public opinion, and their emotions are extremely easy to spread and infect. However, how to capture and analyze the emotional trend of the people, guide the spread of positive public opinion, and ensure the social stability and harmony is an extremely important research topic through the mass public opinion information. As one of the core links in the analysis of public opinion, Chinese text emotion classification has attracted the attention and research of scholars. Because Chinese text data has many characteristics, such as semantic diversity, grammatical particularity, implicit expression and so on, in addition, the current Chinese text emotion classification method mostly belongs to the shallow learning method, and the text representation ability is limited. It is difficult to obtain high accuracy of emotion classification of Chinese text due to the defects of artificial sample extraction. Therefore, how to meet the characteristics of Chinese text and further improve the performance of emotional classification of Chinese text is an urgent need to be studied in the field of network public opinion analysis. Based on the deep learning method, this paper carries out the research of Chinese text emotion classification, and applies it to the analysis of network public opinion. The relevant research contents are as follows: 1. Deep learning emotion classification based on topic fusion. In view of the limitation that only words are used in the traditional deep learning affective classification, this paper combines the topic feature with the depth learning model, and constructs two kinds of deep learning emotion classification model: TBLSTM / TCNN. The two models can combine theme features to obtain high-level text features. The experiments show that the highest classification accuracy of the two models is 91.1% and 91.9% respectively, which is about 2% higher than that of LSTMN CNNRAE and other commonly used deep learning models. Deep learning emotion classification for enhanced feature extraction. In order to improve the feature extraction ability of the model, this paper uses the idea of feature fusion for reference, combines the high-level text features extracted by two kinds of deep learning emotion classification models: TBSP LSTM and TCNN, so as to enhance the text feature extraction ability of the model. And constructed TB_LSTM TCNN emotion classification model. The experimental results show that under the same experimental data, the binary emotion classification accuracy of the model is 0.8-1.6.3. higher than that of TB_LSTM and TCNN. Intelligent modeling of multi-dimensional public opinion analysis based on deep learning. Aiming at the demand of comprehensiveness and accuracy of Chinese network public opinion analysis, this paper constructs a multi-dimensional intelligent analysis model of public opinion based on deep learning. This model can use the enhanced feature extraction depth learning emotion classification model to realize the accurate Chinese text emotion classification, and combines the topic model and the time series model to realize the multi-dimension emotion classification, the multi-dimensional emotion trend analysis and the forecast. 4. An empirical study of multi-dimensional public opinion analysis based on deep learning. In order to verify the validity of the multi-dimensional public opinion analysis model proposed in this paper, this paper takes "Wei Zexi incident" as an empirical case, and realizes the depth analysis of "Wei Zexi event" based on the multi-dimensional intelligent analysis model of public opinion proposed in the paper. The multi-dimensional analysis of the trend of public opinion, the hot spot tracking of public opinion and the depiction of emotional evolution are realized. The validity and practicability of the model are proved by comparison with the expert conclusions.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：G206;G254

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本文編號(hào)：1938929