微博行文具有较大的自由性,其中情感对象识别是一个困难的问题,尤其是情感对象未显性出现情况下的情感对象识别,暂未发现有效解决方法。该文针对这一难题,结合中文微博的特点,提出了一种改进的条件随机场的模型。该模型把情感对象识别看作一个序列标记问题,通过在传统的CRF序列标记模型上增加情感对象的全局节点,有效地结合上下文信息、句法依赖以及情感词典,从而可以识别出微博中的情感对象。该方法的优势在于能够应用于情感对象未显性出现的情况。实验结果表明该方法比现有方法能更有效地识别出微博中的情感对象。
Abstract
Owing to informal words and expressions widely used in micro-blogs, target recognition for the sentiment analysis of microblogs is difficult, especially when the targets are not clearly mentioned. An improved conditional random fields model is proposed to deal with this issue, treating sentiment target extraction as a sequence-labeling problem. Through adding global nodes, the contextual information, syntactic rules and opinion lexicon are considered in the targets extraction. The major contribution of this method is that it can be applied to the texts in which the targets are mentioned in the sequence. Experimental results on the Sina microblog data demonstrate that this method outperforms the state-of-art methods.
关键词
条件随机场 /
微博 /
情感对象识别 /
信息抽取 /
情感分析
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Key words
CRFs /
microblog /
sentiment target /
information extraction /
sentiment analysis
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参考文献
[1]Jiang L, Yu M, Zhou M, et al. Target-dependent Twitter Sentiment Classification[C]//Proceedings of ACL. 2011: 151-160.
[2] Barbosa L, Feng J. Robust sentiment detection on twitter from biased and noisy data[C]//Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, 2010: 36-44.
[3] Hu M, Liu B. Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data mining. ACM, 2004: 168-177.
[4] Hu M, Liu B. Mining opinion features in customer reviews[C]//Proceedings of AAAI. 2004, 4: 755-760.
[5] Popescu A M, Etzioni O. Extracting product features and opinions from reviews[M]//Natural language processing and text mining. Springer London, 2007: 9-28.
[6] Scaffidi C, Bierhoff K, Chang E, et al. Red Opal: product-feature scoring from reviews[C]//Proceedings of the 8th ACM Conference on Electronic Commerce. ACM, 2007: 182-191.
[7] Kobayashi N, Inui K, Matsumoto Y. Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining[C]//Proceedings of EMNLP-CoNLL. 2007: 1065-1074.
[8] Stoyanov V, Cardie C. Topic identification for fine-grained opinion analysis[C]//Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1. Association for Computational Linguistics, 2008: 817-824.
[9] Qiu G, Liu B, Bu J, et al. Opinion word expansion and target extraction through double propagation[J]. Computational linguistics, 2011, 37(1): 9-27.
[10] Ma T, Wan X. Opinion target extraction in Chinese news comments[C]//Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, 2010: 782-790.
[11] 王荣洋, 鞠久朋, 李寿山, 等. 基于 CRFs 的评价对象抽取特征研究[J]. 中文信息学报, 2012, 26(2): 56-61.
[12] 郑敏洁, 雷志城, 廖祥文, 等. 基于层叠 CRFs 的中文句子评价对象抽取[J]. 中文信息学报, 2013, 27(3): 69-76.
[13] 高磊,李斌,戴新宇等.基于依存分析和褒义指向的微博情感队形抽取方法[C]//自然语言处理与中文计算会议(NLP&CC).北京:2012.
[14] 文坤梅,徐帅.基于句法依存关系的微博情感分析方法[C]//自然语言处理与中文计算会议(NLP&CC).北京:2012.
[15] Lafferty J, McCallum A, Pereira F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning
[16] Sutton C, McCallum A. An introduction to conditional random fields[J]. Machine Learning, 2011, 4(4): 267-373.
[17] Nakagawa T, Inui K, Kurohashi S. Dependency tree-based sentiment classification using CRFs with hidden variables[C]//Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2010: 786-794.
[18] Morency L P, Quattoni A, Darrell T. Latent-dynamic discriminative models for continuous gesture recognition[C]//Proceedings of the Computer Vision and Pattern Recognition, IEEE Conference on. IEEE, 2007: 1-8.
[19] Murphy K P, Weiss Y, Jordan M I. Loopy belief propagation for approximate inference: An empirical study[C]//Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., 1999: 467-475.
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脚注
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基金
国家自然科学基金(61100148,61202269);广东省自然科学基金(S2011040004804);广东省科技计划项目(2010B050400011)
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