Abstract
Nowadays, derogatory comments are often made by one another, not only in offline environment but also immensely in online environments like social networking websites and online communities. So, an Identification combined with Prevention System in all social networking websites and applications, including all the communities, existing in the digital world is a necessity. In such a system, the Identification Block should identify any negative online behavior and should signal the Prevention Block to take action accordingly. This study aims to analyze any piece of text and detect different types of toxicity like obscenity, threats, insults and identity-based hatred. The labeled Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A 6-headed Machine Learning tf–idf Model has been made and trained separately, yielding a Mean Validation Accuracy of 98.08% and Absolute Validation Accuracy of 91.64%. Such an Automated System should be deployed for enhancing the healthy online conversation.
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Chakrabarty, N. (2020). A Machine Learning Approach to Comment Toxicity Classification. In: Das, A., Nayak, J., Naik, B., Pati, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-9042-5_16
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DOI: https://doi.org/10.1007/978-981-13-9042-5_16
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