Abstract
This paper presents a learning approach on a unique dataset formulated by authors that detects urgent texts from the posts on social media platforms in Bengali language. It is difficult to keep track of every information we go through social media. In the collision of numerous posts, it is easy to miss information that is urgent. In this advanced era of machine learning, detecting urgent texts among thousands of posts would be much easier if we can implement a model that can filter the urgent text out of them. Therefore, we propose an approach that can identify any type of urgent texts from public posts by leveraging a manually constructed dataset that is fully human annotated. Apart from traditional machine learning classifiers, we applied boosting algorithms in our proposed method in addition. Experimentally, a significant increase in accuracy has been noticed by boosting weak learners. Support Vector Machine (SVM) achieved 80.9% accuracy where gradient boosting outperformed the traditional approach with 82% accuracy while detecting urgent texts in Bengali language.
Rafsan Rahman, Tamanna Nazmin, Noor Nafeur Rahman, Miyad Bhuiyan—These authors contributed equally to this work.
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References
Aipe, A., Mukuntha, N., Ekbal, A., Kurohashi, S.: Deep learning approach towards multi-label classification of crisis related tweets. In: Proceedings of the 15th ISCRAM Conference (2018)
Kayi, E.S., Nan, L., Qu, B., Diab, M., McKeown, K.: Detecting urgencystatus of crisis tweets: a transfer learning approach for low resource languages. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4693–4703 (2020)
Kejriwal, M., Zhou, P.: Low-supervision urgency detection and transfer in short crisis messages In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 353–356 (2019)
Nguyen, D.T., Joty, S., Imran, M., Sajjad, H., Mitra, P.: Applications of online deep learning for crisis response using social media information (2016). arXiv:1610.01030
Imran, M., Elbassuoni, S., Castillo, C., Diaz, F., Meier, P.: Extracting information nuggets from disaster-related messages in social media. In: ISCRAM (2013)
Lampert, A., Dale, R., Paris, C.: Detecting emails containing requests for action. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 984–992 (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-timeevent detection by social sensors. In: Proceedings of the 19th International Conference on Worldwide Web, pp. 851–860 (2010)
Alshehri, Y.A.: Text mining for incoming tasks based on the urgency/importance factors andtask classification using machine learning tools. In: Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis, pp. 183–189 (2020)
Guo, S.X., Sun, X., Wang, S.X., Gao, Y., Feng, J.: Attention-based character-word hybrid neural networks with semantic and structural information for identifying of urgent posts in MOOC discussion forums. IEEE Access 7, 120522–120532 (2019)
Caragea, C., Silvescu, A., Tapia, A.H.: Identifying informative messages in disaster events using convolutional neural networks. In International Conference on Information Systems for Crisis Response and Management, pp. 137–147 (2016)
Sarker, S.: BNLP: Natural language processing toolkit for Bengali language (2021). arXiv:2102.00405
Cavnar, W.B., Trenkle, J.M., et al.: N-gram-based text categorization. In: Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, vol. 161175 (1994)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)
Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann. Stat. 28(2), 337–407 (2000)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189–1232 (2001)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
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Rahman, R., Nazmin, T., Rahman, N.N., Bhuiyan, M., Shahariar, G.M., Shah, F.M. (2022). Urgent Text Detection in Bengali Language Based on Boosting Techniques. In: Hossain, S., Hossain, M.S., Kaiser, M.S., Majumder, S.P., Ray, K. (eds) Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 . Lecture Notes in Networks and Systems, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-19-2445-3_49
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