【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的飛速發(fā)展,微博已經(jīng)成為人們生活中重要的一部分。由微博引發(fā)的輿情也越來越受到各界的關(guān)注。由于微博信息的傳播速度快,傳播范圍廣以及微博消息發(fā)布的任意性,使微博上的信息有真有假,有虛有實(shí)。正面的微博輿情和負(fù)面的微博輿情會(huì)給人們的生活帶來截然不同的影響,有些負(fù)面的微博輿情甚至?xí)䴓?gòu)成危機(jī),嚴(yán)重影響社會(huì)公共安全。因此,對(duì)微博輿情預(yù)測(cè)的研究具有現(xiàn)實(shí)意義。 進(jìn)行微博輿情的預(yù)測(cè),首先要獲取能夠表示微博輿情的數(shù)據(jù)。本文采用離散的時(shí)間序列描述微博輿情的趨勢(shì)。本文以新浪微博平臺(tái)為背景,對(duì)微博文本中的熱點(diǎn)話題進(jìn)行提取、分析并對(duì)微博輿情進(jìn)行預(yù)測(cè)。時(shí)間序列獲取的步驟：一是用新浪微博的API接口,獲取一段時(shí)間內(nèi)的微博文本；二是根據(jù)微博文本的特點(diǎn)進(jìn)行相應(yīng)的預(yù)處理后,使用微博話題統(tǒng)計(jì)的方法,發(fā)現(xiàn)微博熱點(diǎn)話題；三是統(tǒng)計(jì)一段時(shí)間內(nèi)微博熱點(diǎn)話題的回復(fù)數(shù)和轉(zhuǎn)發(fā)數(shù),組成輿情預(yù)測(cè)模型實(shí)驗(yàn)中的時(shí)間序列數(shù)據(jù)。 BP神經(jīng)網(wǎng)絡(luò)能夠較好地?cái)M合微博輿情時(shí)間序列的非線性變化的函數(shù)關(guān)系,能用其預(yù)測(cè)微博輿情,但是也存在著一定的局限性：BP神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)算法對(duì)已經(jīng)學(xué)習(xí)的樣本有遺忘。在樣本中有噪聲時(shí),可能會(huì)使BP神經(jīng)網(wǎng)絡(luò)的性能變差；BP神經(jīng)網(wǎng)絡(luò)還存在收斂速度慢、容易陷入局部極小值的缺點(diǎn)。我們做了兩個(gè)工作：一是對(duì)BP神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)結(jié)構(gòu)進(jìn)行改進(jìn)。在BP神經(jīng)網(wǎng)絡(luò)的輸入層后面添加一個(gè)神經(jīng)元層—輸入承接層,當(dāng)樣本中出現(xiàn)噪聲時(shí),能夠延遲網(wǎng)絡(luò)參數(shù)調(diào)整,從而提高BP神經(jīng)網(wǎng)絡(luò)的性能。二是用遺傳模擬退火算法(GSA)對(duì)BP神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)參數(shù)進(jìn)行優(yōu)化。GSA具有收斂速度快,能夠較好的避免出現(xiàn)局部極小值的問題,從而彌補(bǔ)BP神經(jīng)網(wǎng)絡(luò)的收斂速度慢,容易陷入局部極小值的不足。 本文對(duì)從已有的微博中獲取的輿情時(shí)間序列,分別用四種輿情預(yù)測(cè)模型進(jìn)行輿情預(yù)測(cè)對(duì)比實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,用GSA優(yōu)化的改進(jìn)BP神經(jīng)網(wǎng)絡(luò)的輿情預(yù)測(cè)模型能夠取得較好的預(yù)測(cè)效果。
[Abstract]:With the rapid development of Internet technology, Weibo has become an important part of people's lives. The public opinion caused by Weibo also receives more and more attention from all walks of life. Due to the high speed of Weibo's information, the wide range of dissemination and the arbitrariness of Weibo's news release, the information on Weibo is true and false. The positive public opinion of Weibo and the negative public opinion of Weibo will bring different influence to people's life, and some negative public opinion will even form a crisis, which will seriously affect the social public safety. Therefore, the study of Weibo public opinion prediction has practical significance. Weibo's public opinion prediction, first of all, to obtain the data that can express Weibo public opinion. This paper uses discrete time series to describe the trend of Weibo's public opinion. Based on the platform of Sina Weibo, this paper extracts the hot topics in Weibo's text, analyzes and predicts the public opinion of Weibo. The steps of time series acquisition are as follows: first, using the API interface of Sina Weibo to obtain the Weibo text for a period of time; Third, count the number of replies and retweets of Weibo's hot topics for a period of time. The BP neural network can fit the nonlinear function relation of Weibo's public opinion time series, and can be used to predict the public opinion of Weibo, which is composed of the time series data of public opinion prediction model, the BP neural network can fit the function relation of the nonlinear change of Weibo's public opinion time series, However, there are some limitations in the learning algorithm of the BP neural network. When there is noise in the sample, it may make the performance of BP neural network worse and the BP neural network have the disadvantage of slow convergence rate and easy to fall into local minimum. We have done two works: one is to improve the network structure of BP neural network. After the input layer of BP neural network, we add a neuron layer-input accept layer, which can delay the adjustment of network parameters when there is noise in the sample, so as to improve the performance of BP neural network. Secondly, the genetic simulated annealing algorithm (GSA) is used to optimize the network parameters of BP neural network. The convergence speed of BP neural network is fast, and the problem of local minimum value can be avoided, which can make up for the slow convergence speed of BP neural network. It is easy to fall into the deficiency of local minima. In this paper, the time series of public opinion obtained from Weibo are compared with four models of public opinion prediction. The experimental results show that the improved BP neural network model of public opinion prediction with GSA optimization can achieve better prediction results.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP183;TP393.092

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