Abstract
Cyberbullying is one of the radical emerging problems with the advancements in the Internet, connecting people around the globe by social media networks. Existing studies mostly focus only on cyberbullying detection in the English language, thus the main objective of this paper is to develop an approach to detect cyberbullying in Hindi-English code-mixed language (Hinglish) which is exorbitantly used by Indian users. Due to the unavailability of Hinglish dataset, we created the Hinglish Cyberbullying Comments (HCC) labeled dataset consisting of comments from social media networks such as Instagram and YouTube. We also developed eight different machine learning models for sentiment classification in-order to automatically detect incidents of cyberbullying. Performance measures namely accuracy, precision, recall and f1 score are used to evaluate these models. Eventually, a hybrid model is developed based on top performers of these eight baseline classifiers which perform better with an accuracy of 80.26% and f1-score of 82.96%.
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Tarwani, S., Jethanandani, M., Kant, V. (2019). Cyberbullying Detection in Hindi-English Code-Mixed Language Using Sentiment Classification. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_51
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DOI: https://doi.org/10.1007/978-981-13-9942-8_51
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