發(fā)布時(shí)間：2018-03-20 08:49

  本文選題：微博　切入點(diǎn)：公眾情感分析　出處：《解放軍信息工程大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著Web2.0的興起和迅速發(fā)展,互聯(lián)網(wǎng)上涌現(xiàn)出大量以微博為代表的社交媒體。微博憑借其短小精悍、發(fā)布便捷和更新快速等特點(diǎn),已經(jīng)成為公眾獲取信息和交流情感的重要平臺(tái)。微博話題傳播速度快、社會(huì)影響大,為公眾的信息獲取、分享和傳播提供了便捷的服務(wù),同時(shí)也為敵對(duì)勢(shì)力和不法分子傳播失實(shí)言論、引發(fā)公眾負(fù)面情感提供了渠道。因此,有效的對(duì)微博話題的公眾情感進(jìn)行分析,能夠?yàn)檎�府部門(mén)了解公眾民意和制定高效決策提供支持,對(duì)微博輿論監(jiān)控和引導(dǎo)具有重要意義。本文研究微博話題的公眾情感分析技術(shù),主要包括微博話題追蹤、微博情感分析和微博話題公眾情感分析三個(gè)部分。論文的主要研究成果如下:(1)研究了微博話題追蹤技術(shù),針對(duì)傳統(tǒng)方法往往在微博話題追蹤中忽略了特征之間的語(yǔ)義信息,導(dǎo)致追蹤效果不夠理想的問(wèn)題,提出一種基于詞向量的微博話題追蹤方法。首先,使用神經(jīng)網(wǎng)絡(luò)語(yǔ)言模型在大規(guī)模數(shù)據(jù)集上訓(xùn)練,得到能夠準(zhǔn)確表示詞語(yǔ)語(yǔ)義的詞向量;然后,利用詞向量擴(kuò)展特征向量的語(yǔ)義信息,建立初始話題和微博模糊集合;最后,計(jì)算微博模糊集合和初始話題模糊集合之間的相似度,并依據(jù)設(shè)定閾值進(jìn)行判決,完成話題追蹤。在微博話題語(yǔ)料上進(jìn)行實(shí)驗(yàn),該方法的綜合F1值達(dá)到85.71%,比傳統(tǒng)方法提高了5%,表明基于詞向量的微博話題追蹤方法能夠充分利用詞向量引入的語(yǔ)義信息,從語(yǔ)義層面完成話題追蹤,相比傳統(tǒng)方法能夠有效提高微博話題追蹤性能。(2)研究了微博情感分析技術(shù),針對(duì)傳統(tǒng)的無(wú)監(jiān)督微博情感分析方法不能很好地解決微博語(yǔ)料特征稀疏的問(wèn)題,提出一種基于BTM(Biterm Topic Model)的無(wú)監(jiān)督微博情感分析方法。首先,利用BTM模型對(duì)微博語(yǔ)料中的共現(xiàn)詞對(duì)進(jìn)行建模,挖掘文檔中的隱含主題;然后,利用合并的情感詞典計(jì)算隱含主題的情感分布;最后,結(jié)合文檔的主題分布和主題的情感分布計(jì)算微博的情感傾向,完成情感分析。在NLPCC2012評(píng)測(cè)語(yǔ)料上實(shí)驗(yàn),該方法的平均F1值達(dá)到75.88%,比傳統(tǒng)方法提高了15%,表明基于BTM的無(wú)監(jiān)督微博情感分析方法能夠有效解決微博語(yǔ)料特征稀疏對(duì)情感分析的影響,在無(wú)監(jiān)督的情況下準(zhǔn)確得到微博的情感傾向。(3)研究了微博話題公眾情感分析技術(shù),針對(duì)已有的相關(guān)研究忽視或者不能準(zhǔn)確的對(duì)公眾情感進(jìn)行描述和分析,導(dǎo)致無(wú)法滿足微博輿論監(jiān)控和高效決策需求的問(wèn)題,提出一種有效的微博話題公眾情感分析方法。首先,抽取微博話題的正負(fù)面情感摘要,對(duì)公眾情感進(jìn)行描述;然后,利用提出的三種指標(biāo)對(duì)公眾情感進(jìn)行分析,得到公眾對(duì)話題的情感傾向;最后,利用提出的引導(dǎo)句生成方法來(lái)引導(dǎo)公眾情感。在微博話題語(yǔ)料上進(jìn)行實(shí)驗(yàn),該方法的綜合F1值達(dá)到54.95%,比傳統(tǒng)方法提高了11%,表明該方法不但能夠提高微博話題情感摘要的綜合性能,而且能夠準(zhǔn)確得到公眾對(duì)話題的情感傾向,并有效引導(dǎo)公眾情感。
[Abstract]:With the rise and rapid development of the Web2.0 on the Internet, the emergence of a large number of micro-blog as the representative of the social media. Micro-blog with its convenient and fast update and release characteristics, has become an important platform for public access to information and exchange emotions. Micro-blog topic propagation speed, social influence, access to public information provided convenient service sharing and dissemination, but also for the hostile forces and criminals to spread false statements, causing the public negative emotion provides channels. Therefore, the topic of micro-blog public sentiment and effective analysis, to understand public opinion and support the establishment of efficient decision-making for government departments, has the important meaning to the micro-blog public opinion monitoring and guidance. This paper studies the topic of micro-blog public sentiment analysis technology, including micro-blog micro-blog topic tracking, sentiment analysis and micro-blog the topic of public sentiment analysis three Parts. The main results are as follows: (1) the micro-blog research topic tracking technology, traditional methods are often in the micro-blog topic tracking in ignoring the semantic information between features, resulting in tracking effect is not ideal, put forward a topic tracking method based on micro-blog word vector. Firstly, using neural network language model training in large data sets, can get accurate word semantic vector; then, by using the semantic information word vector expansion feature vector, the establishment of the initial topic and micro-blog fuzzy set; finally, micro-blog calculates fuzzy sets and fuzzy similarity between the initial topic set, and on the basis of threshold judgment, complete the topic tracking. The experiment in the micro-blog topic corpus, the method of the comprehensive F1 value reached 85.71%, 5% higher than the traditional method, that followed the micro-blog word vector based on topic Semantic information can make full use of the word vector is introduced, from the semantic level of topic tracking, compared with the traditional method can effectively improve the performance of topic tracking micro-blog. (2) studied micro-blog emotion analysis technique for unsupervised micro-blog emotion traditional analysis methods can't solve the micro-blog characteristics of corpus sparse problem, proposed one kind based on the BTM (Biterm Topic Model) unsupervised micro-blog sentiment analysis method. First, the micro-blog in the corpus co-occurrence of words using BTM model, mining theme in the document; then calculate the implied theme by emotional distribution combined sentiment dictionary; finally, combined with the theme of emotional distribution distribution and theme of the document calculate the sentiment orientation of micro-blog, complete sentiment analysis. Experiments on the NLPCC2012 corpus, this method of average F1 value reached 75.88%, 15% higher than the traditional method that based on BT Micro-blog M unsupervised sentiment analysis method can effectively solve the influence analysis of micro-blog characteristics of corpus for emotion exactly sparse, emotional tendency of micro-blog without supervision. (3) the micro-blog research topic of public sentiment analysis technology, aiming at the existing research ignored or cannot accurately describe and analyze the public sentiment micro-blog, could not meet the demand of the public opinion monitoring and efficient decision-making problems, put forward a kind of effective micro-blog topic of public sentiment analysis method. First, the positive and negative emotion abstract from micro-blog topic, describe the public sentiment; then, the public sentiment was analyzed by using the three indicators proposed by the public sentiment orientation of the topic finally, using the proposed guidance; sentence generation method to guide the public emotion. Experiments were carried out on the micro-blog topic corpus, the method of the comprehensive F1 value reached 54.95%, compared with the traditional The proposed method improves 11%. It shows that this method can not only improve the comprehensive performance of micro-blog topic sentiment summarization, but also get the public's emotional inclination to the topic accurately, and effectively guide the public emotion.

【學(xué)位授予單位】：解放軍信息工程大學(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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