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Chinese micro-blog sentiment classification through a novel hybrid learning model 预览

Chinese micro-blog sentiment classification through a novel hybrid learning model
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摘要 With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot.Some methods have been developed in the past decade. However, since the Chinese and English are different in language syntax,semantics and pragmatics, sentiment classification methods that are effective for English twitter may fail on Chinese micro-blog. In addition, the colloquialism and conciseness of short Chinese texts introduces additional challenges to sentiment classification. In this work, a novel hybrid learning model was proposed for sentiment classification of Chinese micro-blogs, which included two stages. In the first stage, emotional scores were calculated over the whole dataset by utilizing an improved Chinese-oriented sentiment dictionary classification method. Data with extremely high or low scores were directly labeled. In the second stage, the remaining data were labeled by using an integrated classification method based on sentiment dictionary, support vector machine (SVM) and k-nearest neighbor (KNN). An improved feature selection method was adopted to enhance the discriminative power of the selected features. The two-stage hybrid framework made the proposed method effective for sentiment classification of Chinese micro-blogs.Experiments on the COAE2014 (Chinese Opinion Analysis Evaluation 2014) dataset show that the proposed method outperforms other schemes. With the rising and spreading of micro-blog, the sentiment classification of short texts has become a research hotspot. Some methods have been developed in the past decade. However, since the Chinese and English are different in language syntax, semantics and pragmatics, sentiment classification methods that are effective for English twitter may fail on Chinese micro-blog. In addition, the colloquialism and conciseness of short Chinese texts introduces additional challenges to sentiment classification. In this work, a novel hybrid learning model was proposed for sentiment classification of Chinese micro-blogs, which included two stages. In the first stage, emotional scores were calculated over the whole dataset by utilizing an improved Chinese-oriented sentiment dictionary classification method. Data with extremely high or low scores were directly labeled. In the second stage, the remaining data were labeled by using an integrated classification method based on sentiment dictionary, support vector machine(SVM) and k-nearest neighbor(KNN). An improved feature selection method was adopted to enhance the discriminative power of the selected features. The two-stage hybrid framework made the proposed method effective for sentiment classification of Chinese micro-blogs. Experiments on the COAE2014(Chinese Opinion Analysis Evaluation 2014) dataset show that the proposed method outperforms other schemes.
作者 李芳芳 王欢婷 赵荣昌 刘熙尧 王彦臻 邹北骥 LI Fang-fang;WANG Huan-ting;ZHAO Rong-chang;LIU Xi-yao;WANG Yan-zhen;ZOU Bei-ji;School of Information Science and Engineering,Central South University;'Mobile Health' Ministry of Education–China Mobile Joint Laboratory;College of Computer,National University of Defense Technology;
出处 《中南大学学报:英文版》 SCIE EI CAS CSCD 2017年第10期2322-2330,共9页 Journal of Central South University of Technology
基金 Projects(61573380, 61303185) supported by the National Natural Science Foundation of China Project(13BTQ052) supported by theNational Social Science Foundation of China Project(2016M592450) supported by the China Postdoctoral Science Foundation Project(2016JJ4119) supported by the Hunan Provincial Natural Science Foundation of China.
关键词 CHINESE micro-blog SHORT TEXT HYBRID LEARNING SENTIMENT classification Chinese micro-blog short text hybrid learning sentiment classification
作者简介 Corresponding author: ZHAO Rong-chang, Lecture, PhD; Tel: +86–13647449808; E-mail: byrons.zhao@gmail.com
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