Abstract
Depression is a crucial factor for deciding to suicide. However, few works have been done on depression analysis using the Bengali language based on social media data such as Facebook. In this paper, we propose a depression detection model for Facebook users using Logistic Regression and LSTM. This work aims to analyze the status updates from Facebook users within 2–3 years and evaluate them to detect whether the person is depressed or not. We collected data from 100 users’ profiles from Facebook, containing on average 30 posts from each user, and proposed BenFED dataset. The proposed system considers sixteen emotional factors related to depression. Based on these emotions, Facebook users’ statuses are labeled as four types, e.g., no, mild, moderate, and severe for determining the level of depression. We compared our proposed approach with other state-of-the-art approaches. It is revealed that our proposed approach outperformed most of the compared techniques for detecting emotions, depression levels, and depression statuses.
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Hossen, I., Islam, T., Rashed, M.G., Das, D. (2022). Early Suicide Prevention: Depression Level Prediction Using Machine Learning and Deep Learning Techniques for Bangladeshi Facebook Users. 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_52
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DOI: https://doi.org/10.1007/978-981-19-2445-3_52
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