本文選題：微博 + 情緒分析　； 參考：《南京航空航天大學(xué)》2014年碩士論文

【摘要】：近年來(lái)，微博受到越來(lái)越多的關(guān)注和喜愛(ài)，成為人們表達(dá)個(gè)人情緒和感受的重要平臺(tái)。因此，微博已經(jīng)成為意見(jiàn)挖掘和情感分析的重要資源，吸引了大量專家學(xué)者的關(guān)注和研究。針對(duì)微博進(jìn)行情緒分析可以迅速了解大眾情緒走向并且對(duì)于個(gè)人情緒調(diào)節(jié)有著重要的意義。本文通過(guò)對(duì)微博的研究分析提出了基于詞典的規(guī)則方法識(shí)別微博所表達(dá)的喜、哀、怒、懼、惡、驚六種情緒。 首先，提出以詞典為依據(jù)的基于規(guī)則的方法，通過(guò)實(shí)驗(yàn)詳細(xì)分析了中文情緒詞典在微博情緒分析中的現(xiàn)狀，討論了存在的主要問(wèn)題并深入討論了微博中情緒表達(dá)的語(yǔ)言特點(diǎn)�；诖�，構(gòu)建了兩個(gè)重要的微博情緒分析詞典：微博表情符詞典EmoDic和中文情緒詞典SixDic。其中，，微博表情符詞典EmoDic主要利用互信息方法構(gòu)建，而中文情緒詞典SixDic則是在文本的詞性分析基礎(chǔ)上，將互信息方法與情緒標(biāo)注信息混合篩選的方式獲取。 其次，通過(guò)對(duì)詞典以及微博表達(dá)的分析制定了詳細(xì)的規(guī)則，利用本文構(gòu)建的兩個(gè)詞典進(jìn)行六類情緒識(shí)別實(shí)驗(yàn)。實(shí)驗(yàn)表明，中文情緒詞典SixDic微博情緒分析結(jié)果的覆蓋率達(dá)到65.8%，正確率達(dá)到64%，比同等方法下的大連理工情感本體庫(kù)DUTIR高出12%左右。而表情符詞典EmoDic結(jié)果比人工挑選表情符有更高的召回率，與中文詞典SixDic并用之后，提高情緒分析覆蓋率至80.4%，系統(tǒng)通過(guò)對(duì)表情符加權(quán)和使用否定規(guī)則達(dá)到最佳性能，正確率為74.1%。 最后，選取了一元詞、中文情緒詞典、表情符詞典、否定詞以及標(biāo)點(diǎn)符號(hào)為特征，采用支持向量機(jī)SVM進(jìn)行有監(jiān)督的情緒分類實(shí)驗(yàn)，結(jié)果表明詞典特征在情緒識(shí)別種的效果優(yōu)于一元詞。將SixDic、EmoDic、否定詞和標(biāo)點(diǎn)符號(hào)共用作為特征時(shí)SVM情緒分類結(jié)果最好，達(dá)到61.7%的正確率。實(shí)驗(yàn)結(jié)果表明，在微博細(xì)致情緒識(shí)別中，基于詞典的規(guī)則方法具有明顯的優(yōu)越性。
[Abstract]:In recent years, Weibo has attracted more and more attention and become an important platform for people to express their emotions and feelings. Therefore, Weibo has become an important resource of opinion mining and emotion analysis, and has attracted the attention and research of a large number of experts and scholars. Emotion analysis based on Weibo can quickly understand the trend of public emotion and play an important role in personal emotion regulation. In this paper, based on the analysis of Weibo, a dictionary-based rule method is proposed to identify the six emotions expressed by Weibo: joy, sadness, anger, fear, evil and fear. Firstly, a rule-based approach based on dictionaries is proposed. The present situation of Chinese emotion dictionary in Weibo emotional analysis is analyzed in detail through experiments. The main problems are discussed and the linguistic characteristics of emotion expression in Weibo are discussed in depth. Based on this, two important Weibo emotion analysis dictionaries are constructed: Weibo emoticons dictionary and Chinese emotion dictionary six dictionaries. The Weibo emoticons dictionary is constructed mainly by mutual information method, while the Chinese emotion dictionary SixDic is obtained by mixing mutual information method with emotional tagging information on the basis of part of speech analysis. Secondly, through the analysis of dictionaries and Weibo expressions, the detailed rules are made, and six kinds of emotion recognition experiments are carried out by using the two dictionaries constructed in this paper. The experimental results show that the Chinese emotion Dictionary SixDic Weibo has a coverage rate of 65.8 and a correct rate of 64, which is about 12% higher than that of DUTIR, an emotional ontology library of Dalian University of Science and Technology under the same method. The EmoDic result of emoticons dictionary has a higher recall rate than that of manual selection of emoticons. After being used with six Chinese dictionaries, the emotional analysis coverage is increased to 80.40.The system achieves the best performance by weighting emoji and using negative rules, and the correct rate is 74.1%. Finally, a supervised emotion classification experiment is carried out with support vector machine (SVM), which is based on monologues, Chinese emotion dictionaries, emoji dictionaries, negative words and punctuation marks. The results show that the effect of dictionary features in emotion recognition is better than that of monomorphic words. When six Dictionary EmoDic, negative words and punctuation marks are used as features, SVM has the best result of emotion classification, and the accuracy is 61.7%. The experimental results show that the dictionary-based rule method has obvious advantages in Weibo detailed emotion recognition.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.092

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 歐陽(yáng)純萍;陽(yáng)小華;雷龍艷;徐強(qiáng);余穎;劉志明;;多策略中文微博細(xì)粒度情緒分析研究[J];北京大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年01期

2 楊亮;林原;林鴻飛;;基于情感分布的微博熱點(diǎn)事件發(fā)現(xiàn)[J];中文信息學(xué)報(bào);2012年01期

本文編號(hào)：2065624