Abstract
COVID-19 has caused physical, emotional, and psychological distress for people. Due to COVID-19 norms, people were restricted to their homes and could not interact with other people, due to which they turned to social media to express their state of mind. In this paper, we implemented a system using TensorFlow, which consists of multilayer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM), which works on preprocessing, semantic information on our manually extracted dataset using Twint scraper. The models were used for classifying tweets, based upon whether they indicate depressive behavior or not. We experimented for different optimizer algorithms and their related hyperparameters for all the models. The highest accuracy was achieved by MLP using sentence embeddings, which gave an accuracy of 94% over 50 epochs, closely followed by the other two.
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Kuber, A., Kulthe, S., Kosamkar, P. (2023). Detecting Depression in Tweets Using Natural Language Processing and Deep Learning. In: Joshi, A., Mahmud, M., Ragel, R.G. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2021). Lecture Notes in Networks and Systems, vol 400. Springer, Singapore. https://doi.org/10.1007/978-981-19-0095-2_43
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