Toxic Comment Detection: Analyzing the Combination of Text and Emojis

Detection of toxicity in online commentary is a growing branch of Natural Language Processing (NLP). Most research in the area rely only on text-based toxic comment detection. We propose a machine learning approach for detecting the toxicity of a comment by analyzing both the text and the emojis wit...

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Bibliographic Details
Main Authors: Aquino, Michael, Ortiz, Yasiris, Rashid, Arif, Tumlin, Anne M., Artan, N. Sertac, Dong, Ziqian, Gu, Huanying
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Data models
emojis
Long short term memory
Machine learning
Natural language processing
natural language processing (NLP)
Toxic comments
Toxicology
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Summary:Detection of toxicity in online commentary is a growing branch of Natural Language Processing (NLP). Most research in the area rely only on text-based toxic comment detection. We propose a machine learning approach for detecting the toxicity of a comment by analyzing both the text and the emojis within the comment. Our approach utilizes word embeddings derived from GloVe and emoji2vec to train a bidirectional Long Short Term Memory (biLSTM) model. We also create a new labeled dataset with comments with text and emojis. The accuracy score of our model on preliminary data is 0.911.
ISSN:2155-6814
DOI:10.1109/MASS52906.2021.00097