【摘要】：近年來(lái),隨著社交網(wǎng)絡(luò)的逐漸成熟和移動(dòng)終端技術(shù)的迅猛發(fā)展,微博作為一種網(wǎng)絡(luò)傳播的主要媒體形式,越來(lái)越受到人們的青睞。用戶通過(guò)在微博上表達(dá)觀點(diǎn)傳播思想,抒發(fā)個(gè)人情感的同時(shí),也產(chǎn)生了大量帶有個(gè)人主觀情感特征的信息,這些信息中包含著不同趨向的情感特征,進(jìn)而對(duì)網(wǎng)絡(luò)輿情的傳播能產(chǎn)生巨大的影響。本文使用深度學(xué)習(xí)的方法,對(duì)互聯(lián)網(wǎng)上微博短文本的情感分析問(wèn)題進(jìn)行了相關(guān)研究。具體研究?jī)?nèi)容如下:(1)為了更好的判定微博短文本的情感極性,提出一種基于深度卷積神經(jīng)網(wǎng)絡(luò)模型的情感分類方法。該方法首先將訓(xùn)練的詞向量作為原始特征向量,然后把特征向量送入卷積神經(jīng)網(wǎng)絡(luò)(CNNs,Convolutional Neural Networks)模型進(jìn)一步提取特征,訓(xùn)練出基于該網(wǎng)絡(luò)的情感分類模型,再使用該分類器對(duì)互聯(lián)網(wǎng)短文本進(jìn)行情感分類。實(shí)驗(yàn)比較了基于傳統(tǒng)機(jī)器學(xué)習(xí)的SVM算法與深度學(xué)習(xí)的隨機(jī)生成向量的CNNs模型法和本文提出的方法,最終通過(guò)實(shí)驗(yàn)結(jié)果證明了采用本文方法可以有效的進(jìn)行情感分類。(2)針對(duì)微博短文本中評(píng)價(jià)對(duì)象抽取的問(wèn)題,提出了一種基于雙向長(zhǎng)短時(shí)記憶循環(huán)神經(jīng)網(wǎng)絡(luò)(Bidirectional Long Short-Term Memory,BLSTM)模型的情感要素抽取方法。通過(guò)實(shí)驗(yàn)對(duì)比傳統(tǒng)機(jī)器學(xué)習(xí)模型與循環(huán)神經(jīng)網(wǎng)絡(luò)(RNN,Recurrent Neural Networks)、長(zhǎng)短時(shí)循環(huán)神經(jīng)記憶網(wǎng)絡(luò)(Long Short-Term Memory,LSTM)和雙向長(zhǎng)短時(shí)記憶循環(huán)神經(jīng)網(wǎng)絡(luò)這三種深度學(xué)習(xí)模型發(fā)現(xiàn),采用基于深度學(xué)習(xí)的雙向長(zhǎng)短時(shí)記憶循環(huán)神經(jīng)網(wǎng)絡(luò)模型處理評(píng)價(jià)對(duì)象抽取任務(wù)可以獲得最佳效果。
[Abstract]:In recent years, with the gradual maturity of social network and the rapid development of mobile terminal technology, Weibo, as the main media form of network communication, is more and more popular. By expressing their views on Weibo, users spread their ideas, expressing their personal feelings, and at the same time, they also produced a large amount of information with the characteristics of personal subjective emotions, which contain different trends of emotional characteristics. In turn, the spread of network public opinion can have a huge impact. This paper studies the affective analysis of Weibo's short text on the Internet by using the method of deep learning. The main contents are as follows: (1) in order to better judge the emotional polarity of Weibo's short text, a method of emotion classification based on deep convolution neural network model is proposed. Firstly, the trained word vector is taken as the original feature vector, and then the feature vector is sent into convolution neural network (CNNs,Convolutional Neural Networks) model) to extract features, and then the emotion classification model based on this network is trained. Then we use the classifier to classify the Internet text. The experiment compares the CNNs model method based on the traditional machine learning algorithm with the CNNs model method of the random generating vector of the depth learning and the method proposed in this paper. Finally, the experimental results show that this method can be used to effectively classify emotion. (2) aiming at the problem of evaluation object extraction in Weibo's short text, An emotional element extraction method based on bidirectional long and short term memory loop neural network (Bidirectional Long Short-Term Memory,BLSTM) model is proposed. Compared with traditional machine learning model, cyclic neural network (Long Short-Term Memory,LSTM) and bidirectional long and short term memory circulatory neural network (Long Short-Term Memory,LSTM), we find that, Bidirectional long and short time memory loop neural network model based on deep learning can be used to deal with the evaluation object extraction task and the best result can be obtained.
【學(xué)位授予單位】：中原工學(xué)院
【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)：2201428