Abstract
The growth of social networking sites has been tremendous in the last few decades. It has emerged as a platform to share one’s thoughts and opinions and also interact with new people every day. It is possible to predict a person’s emotions by analysing their tweets and posts over a period of time. The main objective is to detect depression among twitter users by performing a sentiment analysis of their tweets. Real time tweets are extracted from Twitter and detected for negative emotions using a lexicon-based ensemble method and a novel Neutral Negative Scoring algorithm. The user history of potentially “depressed” people is obtained and examined by training a neural network to confirm the onset of depression. The depressed people will be given recommendations for initiating positive actions using a recommendation system. It is found that a Bidirectional Long-Short Term Memory network has the highest accuracy of 90% in detecting users with depression.
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Jyothi Prasanth, D.R., Dhalia Sweetlin, J., Sruthi, S. (2022). Exploring Human Emotions for Depression Detection from Twitter Data by Reducing Misclassification Rate. In: Tiwari, R., Mishra, A., Yadav, N., Pavone, M. (eds) Proceedings of International Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3802-2_10
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DOI: https://doi.org/10.1007/978-981-16-3802-2_10
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