12/27/2023 0 Comments Babelnet chatXia, Chengqing Zong and Shoushan Li, “Ensemble of Feature Sets and Classification Algorithms for Sentiment Classification”, In Information Sciences, vol. Education and Management Engineering, vol. Ahuja, “A Lexical Approach for Opinion Mining in Twitter”, I.J. Information Engineering and Electronic Business, vol.4, pp. Mahanti, “An Improved Information Retrieval Approach to Short Text Classification”, I.J. Modern Education and Computer Science, vol. Singh, “Sentiment Analysis of Twitter User Data on Punjab Legislative Assembly Election”, I.J. Passonneau, “Sentiment Analysis of Twitter Data Proceedings of the Workshop on Languages in Social Media”, Association for Computational Linguistics, Stroudsburg, PA, USA, pp. Wu, "WEFEST: Word Embedding Feature Extension for Short Text Classification", 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, pp. Yang, "Short Text Classification Based on LDA Topic Model", 2016 International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, pp.749-753, 2016. Xiaoyi, "Short Text Classification Based on Wikipedia and Word2vec", 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, pp. Demirbas, “Short Text Classification in Twitter to Improve Information Filtering”, In Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, pp.841-842, 2010. Wang, “Improving Short Text Classification through Better Feature Space Selection”, 2013 Ninth International Conference on Computational Intelligence and Security, Leshan, pp. Xu, "Compositional Recurrent Neural Networks for Chinese Short Text Classification," 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), Omaha, NE,pp.137-144, 2016. Fourth International Conference on, Las Vegas, NV, pp. Spink, "Using Web Search Logs to Identify Query Classification Terms," Information Technology, 2007. Xingang, "Research on Short Text Classification Algorithm Based on Statistics and Rules," 2010 Third International Symposium on Electronic Commerce and Security, Guangzhou, pp. Luo and Junyu Chen, “A Review of Natural Language Processing Techniques for Opinion Mining Systems”, In Information Fusion, vol. Consequently we compared our ensemble model with traditional classification algorithms and observed that the F-measure value is increased. Then the expanded and the original form of the messages are included in an ensemble learning model. In the proposed model first the short messages are expanded with BabelNet which is a concept network. In this paper a sentiment classification model for Twitter messages is proposed to overcome this difficulty. This situation makes the sentiment classification of social media texts more complex. ![]() The users generally express their opinions by using emoticons, abbreviations, slangs, and symbols instead of words. However social media limits the size of user messages. Today, people are chatting with their friends, carrying out social relations, shopping and following many current events through the social media. With the widespread usage of social media in our daily lives, user reviews emerged as an impactful factor for numerous fields including understanding consumer attitudes, determining political tendency, revealing strengths or weaknesses of many different organizations.
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