基于双语信息和神经网络模型的情绪分类方法
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摘要
文本情绪分类是自然语言处理研究中的一项基本任务.目前,已有的文本情绪分类研究大都在单语语料上进行,存在已标注样本不足、分类文本较短、信息量少等问题.为了解决上述问题,提出了一种基于双语信息和神经网络模型的情绪分类方法.首先,利用机器翻译工具对源语料进行翻译得到翻译语料;其次,将对应语言的语料进行合并,得到两组不同语言的语料;最后,将文本分别使用源语言和翻译语言进行特征表示,建立双通道长短期记忆(long short-term memory,LSTM)网络模型融合两组特征,并构建情绪分类器.实验结果表明该方法能够稳定提升文本情绪分类的性能.
Emotion classification was a fundamental learning task in natural language processing. In all existing studies,emotion classification was performed using monolingual information. Due to the challerge of scarity challenge and the limited information in short text,an approach to emotion classification based on bilingual information was proposed. Specially,a machine translation tool was adopted for both Chineseto-English and English-to-Chinese translation. Then,these corpora were merged according to the language. Finally,a two-channel LSTM model was used to leverage both Chinese and English features. The effectiveness of the proposed approach to emotion classification was demonstrated by empirical studies.
Emotion classification was a fundamental learning task in natural language processing. In all existing studies,emotion classification was performed using monolingual information. Due to the challerge of scarity challenge and the limited information in short text,an approach to emotion classification based on bilingual information was proposed. Specially,a machine translation tool was adopted for both Chineseto-English and English-to-Chinese translation. Then,these corpora were merged according to the language. Finally,a two-channel LSTM model was used to leverage both Chinese and English features. The effectiveness of the proposed approach to emotion classification was demonstrated by empirical studies.
引文
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[3] SAIMA A M,SZPAKOWICZ S. Identifying expressions of emotion in text[M].Berlin:Springer,2007,196-205.
[4] BOLLEN J,MAO H N,ZENG X J. Twitter mood predicts the stock market[J]. Journal of computational science,2011,2(1):1-8.
[5] GALIK M,RANK S. Modelling emotional trajectories of individuals in an online chat[M].Berlin:Springer,2012,96-105.
[6] LIU H H,LI S S,ZHOU G D,et al. Joint modeling of news reader's and comment writer's emotions[C]∥Proceedings of the51st Annual Meeting of the Association for Computational Linguistics. Sofia,2013:511-515.
[7]潘云仙,袁方.基于JST模型的新闻文本的情感分类研究[J].郑州大学学报(理学版),2015,47(1):64-68.
[8] KOZAREVA Z,NAVARRO B,VAZQUEZ S,et al. Ua-Zbsa:a headline emotion classification through web information[C]∥Proceedings of the 4th International Workshop on Semantic Evaluation. Prague,2007:334-337.
[9] TOKUHISA R,INUI K,MATSUMOTO Y. Emotion classification using massive examples extracted from the web[C]∥Proceedings of the 22nd International Conference on Computational Linguistics. Manchester,2008:881-888.
[10] BECK D,COHN T,SPECIA L. Joint emotion analysis via multi-task gaussian processes[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha,2014:1798-1803.
[11] BHOWMICK P,BASU A,MITRA P,et al. Multi-label text classification approach for sentence level news emotion analysis[M].Berlin:Springer,2009:261-266.
[12] DAS D,BANDYOPADHYAY S. Sentence level emotion tagging on blog and news corpora[J]. Journal of intelligent systems,2010,19(2):145-162.
[13] LI S S,HUANG L,WANG R,et al. Sentence-level emotion classification with label and context dependence[C]∥Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing,2015:1045-1053.
[14] XU J,XU R F,LU Q,et al. Coarse-to-fine sentence-level emotion classification based on the intra-sentence features and sentential context[C]∥Proceedings of the 21st ACM International Conference on Information and Knowledge Management. Maui,2012:2455-2458.
[15] YANG M,PENG B L,CHEN Z,et al. A topic model for building fine-grained domain-specific emotion lexicon[C]∥Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Baltimore,2014:421-426.
[16] LI S S,XU J,ZHANG D,et al. Two-view label propagation to semi-supervised reader emotion classification[C]∥Proceedings of the 26th International Conference on Computational Linguistics. Osaka,2016:2647-2655.
[17]周胜臣,瞿文婷,石英子,等.中文微博情感分析研究综述[J].计算机应用与软件,2013,30(3):161-164.
[18] HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural computation,1997,9(8):1735-1780.
[19] KINGMA D,BA J. Adam:a method for stochastic optimization[C]∥Proceedings of the 3rd International Conference on Learning Representations. San Diego,2015:1-15.