Twitter Hate Speech Detection using Stacked Weighted Ensemble (SWE) Model
Anomaly Detection; Ensemble Classification; Hard Voting and Soft Voting; Hate Speech; Linear Regression Model; Nae Bayes; Random Forest; Social Networking Services; Tweet Classification; Tweet Filtering
Online Social Media has expanded the freedom of expression in the internet, which has become a disturbing problem if it has an impact on the situation or the interest of a country. Hate speech refers to the use of hostile, abusive or offensive language, directed at a certain group of people who share common property, whether it is their gender, ethnicity or race (i.e. racism), faith and religion. Therefore, auto detection of hate speeches has an increased importance in Online Social Media for filtering any message that has hatred language before posting it to the network. In this paper, a Stacked Weighted Ensemble (SWE) model is proposed for the detection of hate speeches. The model ensembles five standalone classifiers: Linear Regression, Nae Bayes', Random Forest, Hard Voting and Soft Voting. The experimental results on a Twitter dataset has shown an accuracy of 95.54% in binary classification of tweets into hateful speech and an improved performance is noted compared to the standalone classifiers. 2020 IEEE.
Proceedings - 2020 5th International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2020, pp. 87-92.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICRCICN50933.2020.9296199 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099174302&doi=10.1109%2fICRCICN50933.2020.9296199&partnerID=40&md5=b15f7285ec5202571b2d033e37fd1ef1
Kokatnoor S.A., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Krishnan B., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India