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基于中文微博语料的情感倾向性分析 被引量：11

Sentiment analysis on Chinese Micro-blog corpus
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摘要 微博的兴起与传播使得短文本情感分类成为目前的热门研究领域。通过对中文微博语料的情感倾向性分析进行研究,提出了一种新的情感分类方法。首先构建了两级情感词典,并对不同级别情感词作不同增强;然后在情感特征方面使用N-Gram方法,尽量获取有限长度博文中的未登录情感词和情感信息。经实验验证与传统方式相比较,该方法的准确率和召回率都有所提高,在COAE2014微博情感倾向性评测任务中也取得了较好的成绩。 The rise and spread of Micro-blog make sentiment classification on short texts become a hot area.A new method was proposed for Micro-blog sentiment classification.First of all,this method will create an emotional dictiona-ry with two-levels,and the words for different levels will get different enhancement;then in order to get features, N-gram method was used,which found new emotional words and emotional information from a short text.The experi-ment results show this approach has improved precision and recall rate compared to the traditional ways.This algorithm also did a very good job in COAE 2014.
作者 罗毅 李利 谭松波 程学旗
机构地区 中国科学院计算技术研究所
出处 《山东大学学报（理学版）》 CAS CSCD 北大核心 2014年第11期1-7,13,共8页 Journal of Shandong University(Natural Science)
基金 国家自然科学基金资助项目(61232010 61100083) 国家重点基础研究发展计划("九七三"计划)项目(2013CB329601/02) 国家高技术研究发展计划("八六三"计划)项目(2012AA011003) 国家科技支撑计划项目(2012BAH39B04) 国家安全专项项目(2013A140)
关键词 情感分类 倾向性分析 观点挖掘 sentiment classification tendentious analysis opinion mining
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献11

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同被引文献69

  • 1Pang B, Lee L. Seeing stars: Exploiting class relation- ships for sentiment categorization with respect to rating scales[C]//Proceedings o~ the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2005: 115-124.
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  • 3Yih W, He X, Meek C. Semantic parsing for single-rela- tion question answering[C]//Proceedings of ACL 2014.
  • 4Shen Y, He X, Gao J, et al. Learning semantic repre- sentations using convolutional neural networks for web search[C]//Proceedings of the companion publication of the 23rd international conference on World wide web companion. International World Wide Web Confer- ences Steering Committee, 2014: 373-374.
  • 5Blunsom P, Grefenstette E, Kalehbrenner N. A conv- olutional neural network for modelling sentences[C]// Proceedings of the 52nd Annual Meeting of the Associ- ation for Computational Linguistics. 2014.
  • 6Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch[J].The Journal of Ma- chine Learning Research, 2011, 12: 2493-2537.
  • 7dos Santos C N, Gatti M. Deep convolutional neural networks for sentiment analysis of short texts[C]// Proceedings of the 25th International Conference on Computational Linguistics (COLING). Dublin, Ire-land. 2014.
  • 8Kim Y. Convolutional neural networks for sentence classification[C]//Proceedings of the EMNLP,2014.
  • 9Turney P D. Thumbs up or thumbs down? : semantic orientation applied to Unsupervised classification of reviews[C]//Proceedings of the 40th annual meeting on association for computational linguistics. Associa- tion for Computational Linguistics, 2002: 417-424.
  • 10Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks [C]//Advances in neural information processing sys- tems. 2012: 1097-1105.

引证文献11

二级引证文献177

山东大学学报（理学版）
2014年 第11期

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