TY - GEN
T1 - Using Custom Fuzzy Thesaurus to Incorporate Semantic and Reduce Data Sparsity for Twitter Sentiment Analysis
AU - Ismail, Heba M.
AU - Zaki, Nazar
AU - Belkhouche, Boumediene
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/10/2
Y1 - 2017/10/2
N2 - Considerable research efforts have been devoted to Twitter sentiment analysis in recent years. Given the informal writing style of Twitter, there exists an endless variety of sound vocabulary, slogans, emoticons and special characters that can be used to express one's opinion in a maximum of 140-characters. This results in a sparsity problem making the training of machine learning classifiers from Twitter data a highly challenging task. In this work we propose using sentiment replacement of Twitter slogans and incorporating a fuzzy thesaurus for twitter sentiment classification in order to incorporate semantic as well as solve the sparsity problem. The experimental results show that the proposed method consistently outperforms the baselines in addition to some methods in the literature.
AB - Considerable research efforts have been devoted to Twitter sentiment analysis in recent years. Given the informal writing style of Twitter, there exists an endless variety of sound vocabulary, slogans, emoticons and special characters that can be used to express one's opinion in a maximum of 140-characters. This results in a sparsity problem making the training of machine learning classifiers from Twitter data a highly challenging task. In this work we propose using sentiment replacement of Twitter slogans and incorporating a fuzzy thesaurus for twitter sentiment classification in order to incorporate semantic as well as solve the sparsity problem. The experimental results show that the proposed method consistently outperforms the baselines in addition to some methods in the literature.
KW - data sparsity
KW - fuzzy set information retrieval
KW - semantic sentiment
KW - sentiment analysis
KW - text mining
KW - twitter
UR - http://www.scopus.com/inward/record.url?scp=85034655481&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85034655481&partnerID=8YFLogxK
U2 - 10.1109/ISCMI.2016.56
DO - 10.1109/ISCMI.2016.56
M3 - Conference contribution
AN - SCOPUS:85034655481
T3 - Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
SP - 47
EP - 52
BT - Proceedings - 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
Y2 - 23 November 2016 through 25 November 2016
ER -