Abstract
For the study issue of abusive language detection, English is the most commonly employed language. There are just a few works accessible in low-resource languages such as Bangla. People use these sorts of statements on many social media sites. As a result, detection of this type of language is a demand of time. Our goal is to identify this abusive Bangla language in a novel approach. There are some works that use Bengali corpus and transliterated Bengali corpus to detect abusive language. However, in this research, we utilized annotated translated Bengali corpora, and we added a formal justification in each remark for being classified as abusive or non abusive language. For evaluations, we employed a variety of machine learning classifiers where logistic regression achieves 97% accuracy.
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References
Rashid Chowdhury, R., et al.: Analyzing sentiment of movie reviews in Bangla by applying machine learning techniques. In: 2019 International Conference on Bangla Speech and Language Processing (ICBSLP), pp. 1–6 (2019)
Eshan, S.C., Hasan, M.S.: An application of machine learning to detect abusive Bengali text. In: 2017 20th International Conference of Computer and Information Technology (ICCIT), pp. 1–6 (2017)
Hussain, M.G., et al.: An approach to detect abusive Bangla text. In: 2018 International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–5 (2018)
Akter, M., Hossain, M.S., Ahmed, T.U., Andersson, K.: Mosquito classification using convolutional neural network with data augmentation. In: Vasant, P., Zelinka, I., Weber, G.-W. (eds.) ICO 2020. AISC, vol. 1324, pp. 865–879. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68154-8_74
Ghosh, R., et al.: Social media cyberbullying detection using machine learning in Bengali language (2021)
Clark, I., et al.: Deep Learning For Natural Language Processing. https://machinelearningmastery.com/deep-learning-for-nlp/. Accessed 22 Sept 2021
Sazzed, S.: Abusive content detection in transliterated Bengali-English social media corpus. In: Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pp. 125–130 (2021)
Jahan, M., et al.: Abusive comments detection in Bangla-English code-mixed and transliterated text. In: 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–6 (2019)
Islam, T., et al.: An evolutionary approach to comparative analysis of detecting Bangla abusive text. Bull. Electr. Eng. Inform. 10(4), 2163–2169 (2021)
Abdhullah-Al-Mamun, S.A.: Social media bullying detection using machine learning on Bangla text. In: 2018 10th International Conference on Electrical and Computer Engineering (ICECE), pp. 385–388 (2018)
Hussain, M.G., Mahmud, T.A.: A Technique for Perceiving Abusive Bangla Comments (2019)
Emon, E.A., et al.: A deep learning approach to detect abusive Bengali text. In: 2019 7th International Conference on Smart Computing & Communications (ICSCC), pp. 1–5 (2019)
Ishmam, A.M., Sharmin, S.: Hateful speech detection in public Facebook pages for the Bengali language. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 555–560 (2019)
Mahmud, T., Sikder, J., Chakma, R.J., Fardoush, J.: Fabric defect detection system. In: Vasant, P., Zelinka, I., Weber, G.-W. (eds.) ICO 2020. AISC, vol. 1324, pp. 788–800. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68154-8_68
Romim, N., Ahmed, M., Talukder, H., Saiful Islam, M.: Hate speech detection in the Bengali language: a dataset and its baseline evaluation. In: Uddin, M.S., Bansal, J.C. (eds.) Proceedings of International Joint Conference on Advances in Computational Intelligence. AIS, pp. 457–468. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0586-4_37
Mahmud, T., et al.: Face detection and recognition system. In: Peng, S.-L., Hsieh, S.-Y., Gopalakrishnan, S., Duraisamy, B. (eds.) Intelligent Computing and Innovation on Data Science. LNNS, vol. 248, pp. 145–155. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-3153-5_18
Mahmud, T., et al.: An optimal learning model for training expert system to detect uterine cancer. Procedia Comput. Sci. 184, 356–363 (2021)
Islam, R., et al.: A deep learning inspired belief rule-based expert system. IEEE Access 8, 190637–190651 (2020)
Ahmed, T., et al.: An integrated deep learning and belief rule base intelligent system to predict survival of COVID-19 patient under uncertainty. Cogn. Comput. 14(2), 660–676 (2021)
Islam, M., et al.: Belief-rule-based intelligent decision system to select hospital location. Indonesian J. Electr. Eng. Comput. Sci. 1(3), 607 (2016)
Mahmud, T., et al.: Evaluation of job offers using the evidential reasoning approach. Glob. J. Comput. Sci. Technol. (2013)
Mahmud, T., Shahadat Hossain, M.: An evidential reasoning-based decision support system to support house hunting. Int. J. Comput. Appl. 57(21), 51–58 (2012)
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Mahmud, T., Das, S., Ptaszynski, M., Hossain, M.S., Andersson, K., Barua, K. (2023). Reason Based Machine Learning Approach to Detect Bangla Abusive Social Media Comments. In: Vasant, P., Weber, GW., Marmolejo-Saucedo, J.A., Munapo, E., Thomas, J.J. (eds) Intelligent Computing & Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-031-19958-5_46
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