Abstract
A Depression is one of the major causes of suicide nowadays. In the world, most of peoples suffered depression due to personal or professional issues. Most of the peoples share their feelings/sentiments on social media. The sentiments are categorized into positive or negative based on the shared sentiment. If the sentiment is negative then it may lead to suicide. Suicide can be prevented by using resolving the issues. In the present work, depression analysis has been performed with machine learning models such as recurrent neural network, Naive Bayes classifier, and Long Short-Term Memory on the Twitter depression dataset. The results have been quantitively and qualitatively evaluated with accuracy, precision, recall, F1-score, and AROC curve. It has been found that RNN gives 74% accuracy in performing depression classification.
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Singh, S., Chandra, S.K. (2023). Sentiment Analysis for Depression Detection and Suicide Prevention Using Machine Learning Models. In: Garg, L., et al. Key Digital Trends Shaping the Future of Information and Management Science. ISMS 2022. Lecture Notes in Networks and Systems, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31153-6_36
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DOI: https://doi.org/10.1007/978-3-031-31153-6_36
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