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1. 新疆大学信息科学与工程学院
2. 新疆大学新疆多语种信息技术重点实验室
Published:2015
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[1]李响,吐尔根·依布拉音,卡哈尔江·阿比的热西提,等.基于主动学习的SVM维吾尔语情感分析研究[J].新疆大学学报(自然科学版),2015,32(04):447-452.
[1]李响,吐尔根·依布拉音,卡哈尔江·阿比的热西提,等.基于主动学习的SVM维吾尔语情感分析研究[J].新疆大学学报(自然科学版),2015,32(04):447-452. DOI: 10.13568/j.cnki.651094.2015.04.010.
DOI:10.13568/j.cnki.651094.2015.04.010.
在维吾尔语网站和信息平台不断完善的同时
较大规模的带有个人情感的言论也逐渐增多
尤其是微博语料中的情感文本具有很大的现实意义.然而标注语料是一件耗费人力很大的工作.本文在少量已标注语料的基础上针对SVM模型
提出了一种维吾尔语情感分类主动学习方法
加入了聚类代表性、样本差异性、分类不确定性三种策略
实现主动学习的维吾尔语情感分析.实验结果表明
主动学习在维吾尔语情感分类中的有效性
减少了人工标注的工作量
使得情感分类变得更省时省力
也略微提高了SVM的准确率.
With the improvement of the Uyghur language website and information platform
a large number of personal feelings' speech has gradually increased
especially in micro blog corpus of text sentiment has great practical significance. However
tagging corpus is a great waste of human time. Based on a small number of labeled data
this paper proposes a method of Uyghur sentiment classification based on SVM model
which is based on the analysis of the three strategies of clustering
sample and classification. Experimental results show that active learning is effective in the classification of Uyghur language
reducing the workload of manual tagging
making the emotional classification more time and effort
but also slightly improved the accuracy of SVM.
Li S,Huang C R,Zhou G,et al.Employing Personal/Impersonal Views in Supervised and Semi-Supervised Sentiment Classification[C].Proceedings of Annual Meeting of the Association for Computational Linguistics,2010:414-423.
Pang B,Lee L,Vaithyanathan S.Thumbs up?:sentiment classification using machine learning techniques[C].Proceedings of Emnlp,2002:79–86.
Dasgupta S,Ng V.Mine the Easy,Classify the Hard:A Semi-Supervised Approach to Automatic Sentiment Classification[C].Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP,2009,2.
龙军,殷建平,祝恩,等.主动学习研究综述[C].2007全国理论计算机科学学术年会,2007:300-304.
居胜峰,王中卿,李寿山,等.情感分类中不同主动学习策略比较研究[C].中国计算语言学研究前沿进展(2009-2011),2011.
居胜峰.基于主动学习的情感分类方法研究[D].江苏:苏州大学,2013:16-21.
亚森·伊斯马伊力,吐尔根·依布拉音,卡哈尔江·阿比的热西提.基于用户关系的维吾尔文微博数据获取方法的研究[J].新疆大学学报:自然科学版,2015,32(1):74-79.
陈昊,卡哈尔江·阿比的热西提,艾山·吾买尔,等.基于众包的维吾尔语事件标注研究[J].新疆大学学报(自然科学版).2015,32(2):209-214.
徐军,丁宇新,王晓龙.使用机器学习方法进行新闻的情感自动分类[J].中文信息学报,2007,21(6):95-100.
李寿山,黄居仁.基于Stacking组合分类方法的中文情感分类研究[J].中文信息学报,2010,24(5):56-61.
谢丽星,周明,孙茂松.基于层次结构的多策略中文微博情感分析和特征抽取[J].中文信息学报,2012,26(1):73-83.
Pang B,Lee L.A Sentimental Education:Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts[C].Proceedings of the Acl,2004:271–278.
Riloff E,Patwardhan S,Wiebe J.Feature Subsumption for Opinion Analysis[J].In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing(EMNLP-06,2006:440-448.
Mcdonald R,Hannan K,Neylon T,et al.Structured Models for Fine-to-Coarse Sentiment Analysis[C].Proceedings of Annual Meeting of the Association of Computational Linguistics,2007.
于斯音·于苏普,艾斯卡尔·艾木都拉.基于情感词典的维吾尔语文本句子情感分类[J].电脑知识与技术,2014,(4):2371-2374.
禹龙,田生伟,冯冠军.维吾尔语情感词汇自动识别[J].计算机工程,2011,37(7):213-215.
田生伟,禹龙,王宇光.维吾尔语情感分类算法[J].计算机工程与应用,2011,47(36):147-150.
Tong S,Chang E.Support vector machine active learning for image retrieval[C].Proceedings of the ninth ACM international conference on Multimedia.ACM,2001:107–118.
Lewis D D,Gale W A.A Sequential Algorithm for Training Text Classifiers[M].SIGIR’94Springer London,1994:3-12.
徐杰,施鹏飞.图像检索中基于标记与未标记样本的主动学习算法[J].上海交通大学学报,2004,38(12):2068-2072.
Zhou S,Chen Q,Wang X.Active Deep Networks for Semi-Supervised Sentiment Classification[J].Neurocomputing,2010,131:1515-1523.
Vapnik V.The Nature of Statistical Learning Theory 2nd ed[M].Springer New York,1995:998-999.
肖正,刘辉,李兵.一种基于语义距离的Web评论SVM情感分类方法[J].计算机科学,2014,41(9):248-252,284.
力提甫·托乎提.现代维吾尔语参考语法[M].现代维吾尔语参考语法.中国社会科学出版社,2012.
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