Abstract
Depression is one of the most common mental disorders with millions of people suffering from it. It has been found to have an impact on the texts written by the affected masses. In this study, our main aim was to utilize tweets to predict the possibility of a user at-risk of depression through the use of natural language processing (NLP) tools and deep learning algorithms. LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid BiLSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988, respectively, giving an F1 score of 0.9971.
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Shankdhar, A., Mishra, R., Shukla, N. (2022). An Application of Deep Learning in Identification of Depression Among Twitter Users. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_54
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DOI: https://doi.org/10.1007/978-981-16-3071-2_54
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