A probabilistic clustering model for hate speech classification in twitter
作者：
Highlights：
• A probabilistic clustering model for hate speech classification in twitter was developed.
• The use of a naïve Bayes model to improve features representation.
• The use of a modified Jaccard similarity measure for clustering real-time tweet into topic clusters.
• The use of 4-level scale fuzzy model for hate speech classification.
• The Paired Sample t-Test validated the efficiency of the developed model.
摘要
•A probabilistic clustering model for hate speech classification in twitter was developed.•The use of a naïve Bayes model to improve features representation.•The use of a modified Jaccard similarity measure for clustering real-time tweet into topic clusters.•The use of 4-level scale fuzzy model for hate speech classification.•The Paired Sample t-Test validated the efficiency of the developed model.
论文关键词：Twitter,Hate speech,Fuzzy logic,Combinatorial algorithm,Bayesian function,Sentiment analysis
论文评审过程：Received 14 February 2020, Revised 12 February 2021, Accepted 18 February 2021, Available online 24 February 2021, Version of Record 8 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114762
