信息工程

类别区分词与情感词典相结合的维吾尔文句子情感分类

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  • 1. 新疆大学 软件学院, 乌鲁木齐 830046;
    2. 新疆大学 信息科学与工程学院, 乌鲁木齐 830046

收稿日期: 2016-06-22

  网络出版日期: 2017-02-15

Emotion recognition from Uyghur sentences based on combinations of class discrimination words and a sentiment dictionary

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  • 1. School of Software, Xinjiang University, Urumqi 830046, China;
    2. School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China

Received date: 2016-06-22

  Online published: 2017-02-15

摘要

该文在类别区分词特征选择方法的基础上,针对维吾尔文中的生气、高兴、难过、惊讶等句子的情感类别提出了类别区分词与情感词典相结合的方法,进行了句子情感分类研究。结合维吾尔语文本句子中的情感表达特点,利用类别区分词特征选择方法,提取了最有类别区分能力的特征词,并进行了情感分类。通过人工抽取方法收集了维吾尔文句子中能表达情感的关键词,并建立了一个基础情感词典。将该词典与类别区分词结合在一起作为特征,对维吾尔文句子的情感类型有效地进行了分类。实验结果表明类别区分词与情感词典相结合方法的分类效率优于只用类别区分词特征选择方法。

本文引用格式

阿不都萨拉木·达吾提, 于斯音·于苏普, 艾斯卡尔·艾木都拉 . 类别区分词与情感词典相结合的维吾尔文句子情感分类[J]. 清华大学学报(自然科学版), 2017 , 57(2) : 197 -201 . DOI: 10.16511/j.cnki.qhdxxb.2017.22.014

Abstract

This paper presents a recognition method for Uyghur sentence sentiments, such as anger, happiness, sadness and wonder based on combining class-discrimination words (CDW) and a sentiment dictionary. The sentiment expression characteristics in the Uyghur sentence text are identified from features extracted using a CDW feature selection method for the emotion recognition. A set of emotional words is collected manually and put into a sentiment dictionary which is combined with the CDW feature words for the emotion recognition. Tests show that the combined method is more effective than only the CDW feature based method.

参考文献

[1] 宗成庆. 统计自然语言处理[M]. 北京:清华大学出版社, 2013.ZONG Chengqing. Statistical Natural Language Processing[M]. Beijing:Tsinghua University Press, 2013. (in Chinese) [2] 代大明, 王中卿, 李寿山, 等. 基于情绪词的非监督中文情感分类方法研究[J]. 中文信息学报, 2012, 26(4):103-108. DAI Daming, WANG Zhongqing, LI Shoushan, et al. Unsupervised Chinese sentiment classification with emotion words[J]. Journal of Chinese Information Processing, 2012, 26(4):103-108. (in Chinese) [3] 赵志伟. 中文文本倾向性分析研究[D]. 合肥:安徽大学, 2012.ZHAO Zhiwei. Chinese Text Orientation Analysis[D]. Hefei:Anhui University, 2012. (in Chinese) [4] Yang T-H, Hsieh C-T, Soo V-W. Towards text-based emotion detection[C]//International Conference on Information Management and Engineering. Kuala Lumpur, Malaysia, 2009. [5] 李寿山. 情感文本分类方法研究[D]. 北京:中国科学院自动化研究所, 2008.LI Shoushan. Research on Sentiment Classification Method[D]. Beijing:Institute of Automation, Chinese Academy of Sciences, 2008. (in Chinese) [6] 秀段婷, 何婷婷, 宋乐. 基于PMI-IR算法的Blog情感分类研究[C]//第5届全国青年计算语言学研讨会论文集. 武汉:华中师范大学, 2010.XIU Duanting, HE Tingting, SONG Le. Blog sentiment classification based on PMI-IR algorithm[C]//5th National Conference on Computational Linguistics for Young Fellows. Wuhan:Huazhong Normal University China, 2010. (in Chinese) [7] 杨鼎, 阳爱民. 一种基于情感词典和朴素贝叶斯的中文文本情感分类方法[J]. 计算机应用研究, 2010, 27(10):3737-3743.YANG Ding, YANG Aimin. Classification approach of Chinese texts sentiment based on semantic lexicon and naive Bayesian[J]. Application Research of Computers, 2010, 27(10):3737-3743. (in Chinese) [8] 张靖, 金浩. 汉语词语情感倾向自动判断研究[J]. 计算机工程, 2010, 36(23):194-196.ZHANG Jing, JIN Hao. Study on Chinese word sentiment polarity automatic estimation[J]. Computer Engineering, 2010, 36(23):194-196. (in Chinese) [9] 潘文彬. 基于情感词词典的中文句子情感倾向性分析[D]. 北京:北京邮电大学, 2011.FAN Wenbin. Sentiment Orientation Analysis of Chinese Sentences Based on Sentiment Word Dictionary[D]. Beijing:Beijing University of Posts and Telecommunications, 2011. (in Chinese) [10] 王素格, 杨安娜, 李德玉. 基于汉语情感词表的句子情感倾向分类研究[J]. 计算机工程与应用, 2009, 45(24):153-155.WANG Suge, YANG Anna, LI Deyu. Research on sentence sentiment classification based on Chinese sentiment word table[J]. Computer Engineering and Applications, 2009, 45(24):153-155. (in Chinese) [11] 夏睿. 基于语言知识和集成学习的情感文本分类方法研究[D]. 北京:中国科学院自动化研究所, 2011.XIA Rui. Emotional Text Categorization Based on Language Knowledge and Integrated Learning[D]. Beijing:Institute of Automation, Chinese Academy of Sciences, 2011. (in Chinese) [12] 黄俊, 田生伟, 禹龙, 等. 基于维吾尔语情感词的句子情感分析[J]. 计算机工程, 2012, 38(9):183-185.HUANG Jun, TIAN Shengwei, YU Long, et al. Sentence sentiment analysis based on Uyghur sentiment word[J]. Computer Engineering, 2012, 38(9):183-185. (in Chinese) [13] 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(10):2371-2374. Hussein Yusuf, Askar Hamdulla. Sentiment database based sentiment classification from Uyghur text[J]. Computer Knowledge and Technology, 2014(10):2371-2374. (in Chinese) [14] 冯冠军, 禹龙, 田生伟. 基于CRFs自动构建维吾尔语情感词语料库[J]. 现代图书情报技术, 2011(3):17-21.FENG Guanjun, YU Long, TIAN Shengwei. Auto construction of Uyghur emotional words corpus based on CRFs[J]. New Technology of Library and Information Service, 2011(3):17-21. (in Chinese) [15] 热依莱木·帕尔哈提, 孟祥涛, 艾斯卡尔·艾木都拉.基于区分性关键词模型的维吾尔文本情感分类[J]. 计算机工程, 2014, 40(10):132-136.Rayila Parhat, MENG Xiangtao, Askar Hamdulla. Discriminative keyword model based sentiment classification from Uyghur text[J]. Computer Engineering, 2014, 40(10):132-136. (in Chinese) [16] 周奇年, 张振浩, 徐登彩. 用于中文文本分类的基于类别区分词的特征选择方法[J]. 计算机应用与软件, 2013, 30(3):193-195.ZHOU Qinian, ZHANG Zhenhao, XU Dengcai. Feature selection method for Chinese text categorization based on class discriminating words[J]. Computer Applications and Software, 2013, 30(3):193-195. (in Chinese) [17] 祖丽湖玛尔·马木提江. 维吾尔语区分性关键词提取应用软件开发及其性能分析[D]. 乌鲁木齐:新疆大学, 2013.Zulhumar Mamutjan. Uyghur Discriminative Keyword Extraction Software Development[D]. Urumqi:Xinjiang University, 2013. (in Chinese) [18] 周茜, 赵明生, 扈旻. 中文文本分类中的特征选择研究[J]. 中文信息学报, 2004, 18(3):17-23.ZHOU Qian, ZHAO Mingsheng, HU Min. Study on feature selection in Chinese text categorization[J]. Journal of Chinese Information Processing, 2004, 18(3):17-23. (in Chinese) [19] 张玉芳, 王勇, 刘明, 等. 新的文本分类特征选择方法研究[J]. 计算机工程与应用, 2013, 49(5):132-135.ZHANG Yufang, WANG Yong, LIU Ming, et al. New feature selection approach for text categorization[J]. Computer Engineering and Applications, 2013, 49(5):132-135. (in Chinese) [20] 王志昊, 王中卿, 李寿山, 等. 不平衡情感分类中的特征选择方法研究[J]. 中文信息学报, 2013, 27(4):113-118.WANG Zhihao, WANG Zhongqing, LI Shoushan, et al. Feature selection for imbalanced sentiment classification[J]. Journal of Chinese Information Processing, 2013, 27(4):113-118. (in Chinese)
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