Open AccessPerformance analysis of Word Embeddings for Cyberbullying Detection
Author(s) -
Subbaraju Pericherla,
E. Ilavarasan
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1085/1/012008
Subject(s) - word embedding , task (project management) , computer science , word (group theory) , social media , embedding , natural language processing , logistic regression , artificial intelligence , random forest , face (sociological concept) , speech recognition , machine learning , linguistics , world wide web , engineering , philosophy , systems engineering
Cyber bullying activities are increasing day by day with the increase of Social Media Platforms such as Face book, Twitter, Instagram etc. Bullies take the advantage of these large online connected platforms due to which it became as a big challenging task in Natural Language Processing (NLP). In this paper, we compare the performance of various word embedding methods from basic word embedding methods to recent advanced language models such as RoBERTa, XLNET, ALBERT, etc. for cyberbullying detection. We used LightGBM and Logistic regression classifiers for the classification of bullying and non-bullying tweets. Among all the models, RoBERTa is outperformed as compared to state-of-the-art models.