[1]LI Lingxiao,LI Shaozi,CAO Donglin.Emotional multi-source correlation model for chinese micro-blog sentiment analysis[J].CAAI Transactions on Intelligent Systems,2016,11(4):546-553.[doi:10.11992/tis.201605019]
Copy

Emotional multi-source correlation model for chinese micro-blog sentiment analysis

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 4 Page number: 546-553 Column: 学术论文—自然语言处理与理解 Public date: 2016-07-25
Title:
Emotional multi-source correlation model for chinese micro-blog sentiment analysis
Author(s):
LI Lingxiao1; 2;  LI Shaozi1; 2;  CAO Donglin1; 2
1. Cognitive Science Department, Xiamen University, Xiamen 361005, China;
2. Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen 361005, China
Keywords:
multi-modal sentiment analysis; emotional multi-sources; social media; correlation
CLC:
TP391
DOI:
10.11992/tis.201605019
Abstract:
With the explosion of social media information, sentiment analysis of public opinion is attracting more and more attention. Compared with traditional text, the Sina micro-blog contains a variety of emotional sources, including sentiment words, emoticons, pictures, etc. To solve the problem of the poor timeliness of lexicons in Chinese social short messages and to utilize the correlation between different emotional sources, an emotional multi-source correlation model (EMCM) is proposed to carry out sentiment analysis on a micro-blog. In particular, it takes advantage of the correlation between sentiment words and emoticons. It imports the multi-sources and correlation probabilities, and then builds a correlation model between the two emotional sources, emotional words and emoticons, based on a voting model using sentimental words. Experimental results show that this model achieved an accuracy of 85.3% in 6 171 micro-blogs, higher than either the traditional method based on voting (83.4%) or the SVM method based on similar multi-features (82.9%).
References:
[1] PANG Bo, 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, PA, USA:ACM, 2002, 10:79-86.
[2] DAVE K, LAWRENCE S, PENNOCK D M. Mining the Peanut gallery:opinion extraction and semantic classification of product reviews[C]//Proceedings of the 12th International Conference on World Wide Web. Budapest, HU:ACM, 2003:519-528.
[3] YU HONG, HATZIVASSILOGLOU V. Towards answering opinion questions:separating facts from opinions and identifying the polarity of opinion sentences[C]//Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA:ACM, 2003:129-136.
[4] NA J C, SUI H, KHOO C, et al. Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews[C]//MCILWAINE I C. Knowledge Organization and the Global Information Society:Proceedings of the Eighth International ISKO Conference. Wurzburg, Germany:Ergon Verlag, 2004:49-54.
[5] WILSON T, WIEBE J, HOFFMANN P. Recognizing contextual polarity in phrase-level sentiment analysis[C]//Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA:ACM, 2005:347-354.
[6] SCHAPIRE R E, SINGER Y. BoosTexter:a boosting-based system for text categorization[J]. Machine Learning, 2000, 39(2/3):135-168.
[7] 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, PA, USA:ACM, 2002:417-424.
[8] 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.ZHU Yanlan, MIN Jin, ZHOU Yaqian, et al. Semantic orientation computing based on HowNet[J]. Journal of Chinese information processing, 2006, 20(1):14-20.
[9] HU Xia, TANG Jiliang, GAO Huiji, et al. Unsupervised sentiment analysis with emotional signals[C]//Proceedings of the 22nd international conference on World Wide Web. Rio de Janeiro, Brazil:ACM, 2013:607-618.
[10] 赵妍妍, 秦兵, 刘挺. 文本情感分析[J]. 软件学报, 2010, 21(8):1834-1848. ZHAO Yanyan, QIN Bing, LIU Ting. Sentiment analysis[J]. Journal of software, 2010, 21(8):1834-1848.
[11] 魏现辉, 任巨伟, 何文译, 等. DUTIR:中文短文本倾向性分析及要素抽取方法研究[C]//第五届中文倾向性分析评测研讨会论文集. 太原, 2013:116-129. WEI Xianhui, REN Juwei, HE Wenyi, et al. DUTIR:method research of sentiment analysis and elements extraction of Chinese short text[C]//Proceedings of the Fifth Chinese Opinion Analysis Evaluation. Taiyuan, 2013:116-129.
[12] 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.XIE Lixing, ZHOU Ming, SUN Maosong. Hierarchical structure based hybrid approach to sentiment analysis of Chinese micro blog and its feature extraction[J]. Journal of Chinese information processing, 2012, 26(1):73-83.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems