Toxic Comment Classification using Deep Learning

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B. Ramesh Naidu
Naresh Tangudu
Chandra Sekhar
K. Kavitha
B. V. Ramana
P.Venkateswarlu Reddy
Jayavardhanarao Sahukaru
Raj Ganesh Lopinti


Online Conversation media serves as a means for individuals to engage, cooperate, and exchange ideas; however, it is also considered a platform that facilitates the spread of hateful and offensive comments, which could significantly impact one's emotional and mental health. The rapid growth of online communication makes it impractical to manually identify and filter out hateful tweets. Consequently, there is a pressing need for a method or strategy to eliminate toxic and abusive comments and ensure the safety and cleanliness of social media platforms. Utilizing LSTM, Character-level CNN, Word-level CNN, and Hybrid model (LSTM + CNN) in this toxicity analysis is to classify comments and identify the different types of toxic classes by means of a comparative analysis of various models. The neural network models utilized for this analysis take in comments extracted from online platforms, including both toxic and non-toxic comments. The results of this study can contribute towards the development of a web interface that enables the identification of toxic and hateful comments within a given sentence or phrase, and categorizes them into their respective toxicity classes.

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Naidu, B. R. ., Tangudu, N. ., Sekhar, C. ., Kavitha, K. ., Ramana, B. V., Reddy, P. . ., Sahukaru, J. ., & Lopinti, R. G. . (2023). Toxic Comment Classification using Deep Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7), 93–104.


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