本文選題：微博 + 情感分析��； 參考：《安徽大學(xué)》2014年碩士論文

【摘要】：二十一世紀(jì)以來,中國互聯(lián)網(wǎng)行業(yè)得到了蓬勃的發(fā)展,網(wǎng)民規(guī)模也逐年攀升。微博是近年來互聯(lián)網(wǎng)上越來越流行的消遣方式,上到政商名流,下至普通百姓,皆樂在其中,微博已逐漸變成了許多人生活中不可缺少的元素。新浪微博平臺每天都產(chǎn)生了數(shù)以億計的微博來分享內(nèi)容、傳播信息,這龐大的用戶量和數(shù)據(jù)量背后伴隨而來的則是潛藏的商業(yè)、社會等多方面價值。 對微博進行情感分析的研究,就是發(fā)掘微博潛藏的商業(yè)、社會等多方面價值的過程,研究微博情感分析能應(yīng)用于輿情發(fā)現(xiàn)及監(jiān)控、信息預(yù)測、產(chǎn)品評價及改進等領(lǐng)域。深入研究微博內(nèi)容、獲取微博情感傾向是非常有必要的。 目前的微博情感極性分類方法存在著準(zhǔn)確率較低、依賴領(lǐng)域知識、較少考慮句內(nèi)句間關(guān)系等缺點,我們的研究希望找到一種方法使分類準(zhǔn)確率能得到提高,方法的普適性能得到加強�；诖顺霭l(fā)點,本文對結(jié)合情感詞典與規(guī)則的微博情感分析方法進行了研究,主要內(nèi)容包括以下兩個部分： (一)本文通過構(gòu)建情感詞典,獲取語義規(guī)則,以情感詞為中心,歸納了6種情感詞組合,兼顧情感詞、否定詞、程度副詞之間的相互作用,結(jié)合情感詞典與規(guī)則,運用微博子句情感值、整句情感值計算方法,最終實現(xiàn)了微博情感極性分類。實驗表明,本文提出的方法比表情符號判別法、情感詞典判別法、SVM判別法等方法的微博情感極性分類效果都好。 (二)本文在(一)的基礎(chǔ)上,研究轉(zhuǎn)折連詞對微博情感表達(dá)的影響,從轉(zhuǎn)折連詞的4種一般使用情形,考慮微博的句內(nèi)關(guān)系、句間關(guān)系,引入轉(zhuǎn)折連詞權(quán)重系數(shù)來改進(一)的微博子句情感值、整句情感值計算方法,提升微博情感極性分類效果。實驗表明,考慮轉(zhuǎn)折連詞的方法比之前方法分類效果得到了提升。整體實驗對比驗證了本文所提出的方法不依賴領(lǐng)域知識,普適性較強,準(zhǔn)確率較高。
[Abstract]:Since the 21 century, the Internet industry in China has been booming, and the scale of Internet users has been rising year by year. Weibo is a more and more popular pastime on the Internet in recent years, from the political and commercial celebrities to the ordinary people, they all enjoy it. Weibo has gradually become an indispensable element in the life of many people. Sina Weibo platform produces hundreds of millions of Weibo every day to share content and spread information, this huge number of users and data is accompanied by hidden commercial, social and other values. The research on Weibo's emotion analysis is the process of discovering the commercial and social value hidden by Weibo, and studying the affective analysis of Weibo can be applied in the fields of public opinion discovery and monitoring, information prediction, product evaluation and improvement and so on. It is very necessary to study Weibo's content in depth and to obtain Weibo's emotional tendency. The current classification methods of Weibo's affective polarity have some disadvantages, such as low accuracy, dependence on domain knowledge, less consideration of the relationship between sentences, etc. Our research hopes to find a method to improve the classification accuracy. The universality of the method is enhanced. Based on this starting point, this paper studies Weibo's affective analysis method combined with emotion dictionary and rules. The main content includes the following two parts: (1) by constructing the emotion dictionary, obtaining the semantic rules, taking the emotion word as the center, this paper sums up six kinds of emotion words combination, which takes into account the interaction between emotion words, negative words and degree adverbs, and combines the emotion dictionary with the rules. By using Weibo clause emotion value and the whole sentence emotion value calculation method, we have finally realized Weibo emotion polarity classification. The experimental results show that the proposed method is better than the emoji discriminant method, the emotion dictionary discriminant method and SVM discriminant method in the classification of Weibo's affective polarity. (2) on the basis of (1), this paper studies the influence of turning conjunctions on Weibo's emotional expression. From the four general usage situations of turning conjunctions, we consider the intra-sentence and inter-sentence relations of Weibo. The weight coefficient of turning conjunction is introduced to improve (1) the calculation method of Weibo clause emotion value and the whole sentence emotion value to improve the effect of Weibo affective polarity classification. The experimental results show that the classification effect of the method is better than that of the previous method. The overall experimental results show that the proposed method does not rely on domain knowledge, and is more general and accurate.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092;TP391.1

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