【摘要】：信息社會(huì)信息傳遞的方式多種多樣，通過(guò)微博這種便捷的信息交流方式，信息的傳遞已經(jīng)深入我們生活各個(gè)角落。由于在微博平臺(tái)上擁有數(shù)以萬(wàn)計(jì)的用戶，而且經(jīng)常會(huì)在微博上發(fā)表對(duì)于某件事情或者某一熱點(diǎn)話題的討論帶有個(gè)人感情色彩的見(jiàn)解。因此，對(duì)微博平臺(tái)上留存的大量的語(yǔ)料進(jìn)行分析，可發(fā)現(xiàn)大多數(shù)人群普遍的情緒、情感和價(jià)值取向，可為關(guān)心相關(guān)問(wèn)題的決策者提供分析問(wèn)題的依據(jù)。 本文首先對(duì)已有的語(yǔ)料的情感分析的相關(guān)的研究進(jìn)行了歸納與總結(jié)。隨后，比較了幾種常用的情感分類模型，包括基于相似度的方法、貝葉斯分類器、支持向量機(jī)等。通過(guò)對(duì)各個(gè)模型的優(yōu)、缺點(diǎn)進(jìn)行分析，最終，采用目前廣泛認(rèn)可的一種情感分類方法——條件隨即場(chǎng)（CRFs）；其次，采用詞語(yǔ)粒度級(jí)別上對(duì)文本中的中文句子進(jìn)行特征性的標(biāo)注，利用條件隨即場(chǎng)模型對(duì)實(shí)驗(yàn)的語(yǔ)料進(jìn)行訓(xùn)練，形成訓(xùn)練模型，，運(yùn)用訓(xùn)練好的模型對(duì)評(píng)論信息進(jìn)行情感傾向性的判定。最后，提出一種情感強(qiáng)弱的分級(jí)機(jī)制，使得情感分析不僅僅局限于正面、中性以及反面三種情況，實(shí)驗(yàn)結(jié)果量化了原有的三個(gè)方面，從而通過(guò)量化后的結(jié)果，對(duì)情感的強(qiáng)弱進(jìn)行排名。 本文通過(guò)使用CRFs對(duì)語(yǔ)料的分析后得出的結(jié)果來(lái)看，CRFs對(duì)于情感語(yǔ)句具有較好的分類效果，而且運(yùn)用實(shí)驗(yàn)結(jié)果基本上驗(yàn)證了作者提出的情感強(qiáng)弱分級(jí)的機(jī)制的可行性，通過(guò)量化的結(jié)果可為決策者提供數(shù)據(jù)的支撐。但研究中仍有需要改進(jìn)的地方，如語(yǔ)料庫(kù)仍不十分完備等問(wèn)題，日后會(huì)進(jìn)一步完善。
[Abstract]:There are many ways of information transmission in information society. Weibo is a convenient way to communicate information, and the transmission of information has gone deep into every corner of our life. With tens of thousands of users on the Weibo platform, and often on Weibo, personal insights into the discussion of something or a hot topic are posted. Therefore, the analysis of a large number of corpus retained on the Weibo platform can find that the general emotion, emotion and value orientation of most people can provide the basis for the decision makers concerned about the related problems to analyze the problems. First of all, this paper summarizes the related research of emotional analysis of the existing corpus. Then, several commonly used affective classification models are compared, including similarity-based methods, Bayesian classifiers, support vector machines, and so on. Based on the analysis of the advantages and disadvantages of each model, finally, a widely accepted emotion classification method, conditional Random Field (CRFs);, is adopted at the end of the paper. Secondly, the Chinese sentences in the text are marked at the level of word granularity, and the experimental corpus is trained by the conditional random field model to form a training model. The trained model is used to judge the emotional tendency of the comment information. Finally, a classification mechanism of emotion strength is proposed, which makes emotional analysis not only confined to positive, neutral and negative cases, but also quantifies the original three aspects of the experimental results, so as to pass the quantized results. Rank the strength of the emotion. Through the analysis of the corpus by using CRFs, this paper shows that CRFs has a good classification effect for affective sentences, and the experimental results basically verify the feasibility of the mechanism of emotional intensity classification proposed by the author. Quantitative results can provide data support for decision makers. However, there are still some problems that need to be improved, such as the incomplete corpus and so on, which will be further improved in the future.
【學(xué)位授予單位】：東北師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092

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