基于词性标注序列特征提取的微博情感分类

doi:10.11772/j.issn.1001-9081.2014.10.2869

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2869-2873.DOI: 10.11772/j.issn.1001-9081.2014.10.2869

基于词性标注序列特征提取的微博情感分类

卢伟胜,郭躬德,陈黎飞

福建师范大学数学与计算机科学学院，福州 350007

收稿日期:2014-04-28 修回日期:2014-06-12 发布日期:2014-10-30 出版日期:2014-10-01
通讯作者: 郭躬德
作者简介:卢伟胜(1990-)，男，福建漳州人，硕士研究生，主要研究方向：数据挖掘、人工智能；
郭躬德(1965-)，男，福建龙岩人，教授，博士，主要研究方向：数据挖掘、机器学习；
陈黎飞(1972-)，男，福建长乐人，副教授，博士，主要研究方向：数据挖掘、模式识别。
基金资助:
国家自然科学基金资助项目

Emotion classification with feature extraction based on part of speech tagging sequences in micro blog

LU Weisheng,GUO Gongde,CHEN Lifei

School of Mathematics and Computer Science, Fujian Normal University, Fuzhou Fujian 350007, China

Received:2014-04-28 Revised:2014-06-12 Online:2014-10-30 Published:2014-10-01
Contact: GUO Gongde

摘要/Abstract

摘要：

传统的n-gram文本特征提取方法会产生高维度的特征向量，高维数据不但增大了分类的难度，同时也会增加分类的时间。针对这一问题，提出了一种基于词性（POS）标注序列的特征提取方法，根据词性序列能够代表一类文本的这一个特点，利用词性序列组作为文本的特征以达到降低特征维度的效果。在实验中，词性序列特征提取方法比n-gram特征提取方法至少提高了9%的分类精度，降低4816个维度。实验结果表明，该方法能够适用于微博情感分类。

Abstract:

Traditional n-gram feature extraction tends to produce a high-dimensional feature vector. High-dimensional data not only increases the difficulty of classification, but also increases the classification time. Aiming at this problem, this paper presented a feature extraction method based on Part-of-Speech (POS) tagging sequences. The principle of this method was to use POS sequences as text features to reduce feature dimension, according to the property that POS sequences can represent a kind of text.In the experiment,compared with the n-gram feature extraction, the feature extraction based on POS sequences at least improved the classification accuracy of 9% and reduced the dimension of 4816. The experimental results show that the method is suitable for emotion classification in micro blog.

中图分类号:

TP391.1

卢伟胜郭躬德陈黎飞. 基于词性标注序列特征提取的微博情感分类[J]. 计算机应用, 2014, 34(10): 2869-2873.

LU Weisheng GUO Gongde CHEN Lifei. Emotion classification with feature extraction based on part of speech tagging sequences in micro blog[J]. Journal of Computer Applications, 2014, 34(10): 2869-2873.

参考文献

［1］LEVENBERG A, PULMAN S, MOILANEN K, et al.Predicting economic indicators from Web text using sentiment composition ［J］. International Journal of Computer and Communication Engineering, 2014,3(2):109-115.
［2］ZHANG X, FUEHRES H, GLOOR P A. Predicting stock market indicators through twitter "I hope it is not as bad as I fear" ［J］. Procedia-Social and Behavioral Sciences, 2011,26:55-62.
［3］CAVNAR W B, TRENKLE J M. N-gram-based text categorization ［C］// Proceedings of the 1994 3rd Annual Symposium on Document Analysis and Information. Las Vegas: [s.n.], 1994:161-175.
［4］JONES K S. A statistical interpretation of term specificity and its application in retrieval ［J］. Journal of Documentation, 1972,28(1):11-21.
［5］KOULOUMPIS E, WILSON T, MOORE J. Twitter sentiment analysis: the good the bad and the OMG! ［C］// Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2011:538-541.
［6］PANG B, LEE L, VAITHYANATHAN S. Thumbs up? sentiment classification using machine learning techniques ［C］// Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2002,10:79-86.
［7］ZHAO Y, QIN B, LIU T. Sentiment analysis ［J］. Journal of Software, 2010,21(8):1834-1848.(赵妍妍,秦兵,刘挺.文本情感分析［J］.软件学报,2010,21(8):1834-1848.)
［8］ZHU Y, MIN J, ZHOU Y, et al.Semantic orientation computing based on HowNet ［J］. Journal of Chinese Information Processing, 2006,20(1):14-20.(朱嫣岚,闵锦,周雅倩,等.基于HowNet的词汇语义倾向计算［J］.中文信息学报,2006,20(1):14-20.)
［9］WANG Z, WU Z, HU F. Words sentiment polarity calculation based on HowNet and PMI ［J］. Computer Engineering, 2012,38(15):187-189.(王振宇,吴泽衡,胡方涛. 基于HowNet和PMI的词语情感极性计算［J］.计算机工程,2012,38(15):187-189.)
［10］ZHANG C, LIU P, ZHU Z, et al.A sentiment analysis method based on a polarity lexicon ［J］. Journal of Shandong University: Natural Science, 2012,47(3):47-50.(张成功,刘培玉,朱振方,等.一种基于极性词典的情感分析方法［J］.山东大学学报：理学版,2012,47(3):47-50.)
［11］TURNEY P, LITTMAN M. Measuring praise and criticism: inference of semantic orientation from association ［J］. ACM Transactions on Information Systems, 2003,21(4):315-346.
［12］DAVIDOV D, TSUR O, RAPPOPORT A. Enhanced sentiment learning using twitter hashtags and smileys ［C］// Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Stroudsburg: Association for Computational Linguistics, 2010:241-249.
［13］PANG L, LI S, ZHOU G. Sentiment classification method of Chinese micro-blog based on emotional knowledge ［J］. Computer Engineering, 2012,38(13):156-158.(庞磊,李寿山,周国栋.基于情绪知识的中文微博情感分类方法［J］.计算机工程,2012,38(13):156-158.)
［14］TANG H, TAN S, CHENG X. Research on sentiment classification of Chinese reviews based on supervised machine learning techniques ［J］. Journal of Chinese Information Processing, 2007,21(6):88-94.(唐慧丰,谭松波,程学旗.基于监督学习的中文情感分类技术比较研究［J］.中文信息学报,2007,21(6):88-94.)
［15］GIMPEL K, SCHNEIDER N, OCONNOR B, et al.Part-of-speech tagging for twitter: annotation, features, and experiments ［C］// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2011,2:42-47.
［16］WILKS Y, STEVENSON M. The grammar of sense: using part-of-speech tags as a first step in semantic disambiguation ［J］. Natural Language Engineering, 1998,4(2):135-143.
［17］TURNEY 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. Stroudsburg: Association for Computational Linguistics, 2002:417-424.
［18］LUO R, FENG Z, LIU J. Prediction of protein structural class by amino acid and polypeptide composition ［J］. European Journal of Biochemistry, 2002,269(17):4219-4225.
［19］CHOU K, CAI Y. Predicting protein structural class by functional domain composition ［J］. Biochemical and Biophysical Research Communications, 2004,321(4):1007-1009.
［20］CHANG C, LIN C. LIBSVM: a library for support vector machines ［EB/OL］. ［2014-01-26］. http://www.csie.ntu.edu.tw/~cjlin/libsvm.

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基于词性标注序列特征提取的微博情感分类

Emotion classification with feature extraction based on part of speech tagging sequences in micro blog

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