卷积神经网络下的Twitter文本情感分析
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摘要
随着社交网络的日益普及,基于Twitter文本的情感分析成为近年来的研究热点。Twitter文本中蕴含的情感倾向对于挖掘用户需求和对重大事件的预测具有重要意义。但由于Twitter文本短小和用户自身行为存在随意性等特点,再加之现有的情感分类方法大都基于手工制作的文本特征,难以挖掘文本中隐含的深层语义特征,因此难以提高情感分类性能。本文提出了一种基于卷积神经网络的Twitter文本情感分类模型。该模型利用word2vec方法初始化文本词向量,并采用CNN模型学习文本中的深层语义信息,从而挖掘Twitter文本的情感倾向。实验结果表明,采用该模型能够取得82.3%的召回率,比传统分类方法的分类性能有显著提高。
With the increasing popularity of social networks,sentiment analysis based on Twitter text has become a hotspot in recent years.The sentiment tendencies contained in tweets are important for mining user needs and predicting major events.However,the existing sentiment classification methods are mostly based on hand-made text features,and it is hard to mine implicit deep semantics of texts.In addition,because of special characteristics,such as short text and arbitrariness of users′behavior,it is more difficult to improve performance of current sentiment classification.This paper presents a novel Twitter sentiment classification model based on convolutional neural network(CNN).In order to explore sentiment tendency of tweets,the proposed model utilizes a dynamic CNN architecture to learn deep semantics from tweets,which initializes input word embedding with word2 vec method.Experimental results show that our proposed model can achieve a recall rate of 82.3%,which is much higher than performances of traditional classification methods.
With the increasing popularity of social networks,sentiment analysis based on Twitter text has become a hotspot in recent years.The sentiment tendencies contained in tweets are important for mining user needs and predicting major events.However,the existing sentiment classification methods are mostly based on hand-made text features,and it is hard to mine implicit deep semantics of texts.In addition,because of special characteristics,such as short text and arbitrariness of users′behavior,it is more difficult to improve performance of current sentiment classification.This paper presents a novel Twitter sentiment classification model based on convolutional neural network(CNN).In order to explore sentiment tendency of tweets,the proposed model utilizes a dynamic CNN architecture to learn deep semantics from tweets,which initializes input word embedding with word2 vec method.Experimental results show that our proposed model can achieve a recall rate of 82.3%,which is much higher than performances of traditional classification methods.
引文
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[2] Ku L W,Wu T H,Lee L Y,et al.Construction of an evaluation corpus for opinion extraction[C]∥Proceedings of NTCIR-5Workshop Meeting.Tokyo,Japan:[s.n.],2005.
[3] Pang B,Lee L,Vaithyanathan S.Thumbs up?:Sentiment classification using machine learning techniques[C]∥Proceedings of the ACL-02Conference on Empirical Methods in Natural Language Processing.Philadelphia,USA:Association for Computational Linguistics,2002:79-86.
[4] Ekmekcioglu F C,Lynch M F,Willett P.Stemming and n-gram matching for term conflation in Turkish texts[J].Information Research News,1996,7(1):2-6.
[5] Kouloumpis E,Wilson T,Moore J.Twitter sentiment analysis:The good the bad and the omg[C]∥International Conference on Weblogs and Social Media.Barcelona,Spain:[s.n.],2011,11:164.
[6] Bouamor D,Semmar N,Zweigenbaum P.Identifying bilingual multi-word expressions for statistical machine translation[C]∥International Conference on Language Resources and Evaluation.Istanbul,Turkey:[s.n.],2012:674-679.
[7] Kennedy A,Inkpen D.Sentiment classification of movie reviews using contextual valence shifters[J].Computational Intelligence,2006,22(2):110-125.
[8] Xavier G,Antoine B,Yoshua B.Domain adaptation for large-scale sentiment classification:A deep learning approach[C]∥Proceedings of the 28th International Conference on Machine Learning.Washington:ACM,2011:97-110.
[9] Paccanaro A,Hinton G E.Learning distributed representations of concepts using linear relational embedding[J].IEEE Transactions on Knowledge and Data Engineering,2001,13(2):232-244.
[10]Tomas M,Stefan K,Lukas B,et al.Extensions of recurrent neural network language model[C]∥2011IEEE International Conference on Acoustics,Speech and Signal Processing.Prague,Czech Republic:IEEE,2011:5528-5531.
[11]Collobert R,Weston J,Bottou L,et al.Natural language processing(almost)from scratch[J].Journal of Machine Learning Research,2011,12:2493-2537.
[12]Hu M,Liu B.Mining and summarizing customer reviews[C]∥Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2004:168-177.
[13]Shen Y,He X,Gao J,et al.Learning semantic representations using convolutional neural networks for web search[C]∥Proceedings of the 23rd International Conference on World Wide Web.Korea:ACM,2014:373-374.
[14]王伟,周咏梅,阳爱民,等.基于种子词的微博表情符情感倾向判定方法[J].数据采集与处理,2017,32(1):198-204.Wang Wei,Zhou Yongmei,Yang Aimin,et al.Determination method for sentiment orientation of microblog smileys based on seed words[J].Journal of Data Acquisition and Processing,2017,32(1):198-204.
[15]Bengio Y,Ducharme R,Vincent P,et al.A neural probabilistic language model[J].Journal of Machine Learning Research,2003,3:1137-1155.
[16]Ronan C,Jason W.A unified architecture for natural language processing:Deep neural networks with multitask learning[C]∥Proceedings of the 25th International Conference on Machine Learning.New York,USA:ACM,2008:160-167.
[17]Mikolov T,Sutskeer I,Chen K,et al.Distributed representations of words and phrases and their compositionality[J].Advances in Neural Information Processing Systems,2013,26:3111-3119.