Abstract
With the expansion of digital sphere and advancement of technology, cyberbullying has become increasingly common, especially among teenagers. In this work, we have created a benchmark Hindi-English code-mixed corpus called BullySent, annotated with bully and sentiment labels for investigating how sentiment label information helps to identify cyberbully in a better way. For a vast portion of India, both of these languages constitute the primary means of communication, and language mixing is common in everyday speech. A multi-task framework called MT-BERT+VecMap based on two different embedding schemes for the efficient representations of code-mixed data, has been developed. Our proposed multi-task framework outperforms all the single-task baselines with the highest accuracy values of 81.12(+/−1.65)% and 77.46(+/−0.99)% for the cyberbully detection task and sentiment analysis task, respectively.
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The Authors would like to acknowledge the support of Ministry of Home Affairs (MHA), India for conducting this research.
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Maity, K., Saha, S. (2021). A Multi-task Model for Sentiment Aided Cyberbullying Detection in Code-Mixed Indian Languages. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_36
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