Depression Identification Through Tweet Clusters

Depression Identification Through Tweet Clusters

Abhishek Masand, Suryansh Chauhan, Tarun Jain
Copyright: © 2022 |Pages: 14
DOI: 10.4018/IJSI.297916
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Abstract

Over the past few years, the awareness and popularity of Mental health have been on a rapid rise and people are becoming more aware of the surrounding problems. This has helped for mental illnesses like Depression to become recognized and be treated appropriately. Social media has played an integral part in this uproar due to its increased popularity and ease of use. This has allowed people to spread awareness, seek help and vent out their emotions. Our paper is a comparative study of different models for detecting depression with real-time Twitter data and proposing the best performing model. For depression detection, a collection of tweets per user spread over time was used. The data was augmented and then passed through the deep learning model to identify depression in Twitter users based on their Time-Distributed tweets. The proposed model achieved an accuracy of over 90%.
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Background

Using Social Media for Sentiment Analysis is a growing trend these and as more users join these platforms, more data is available. This has spurred on a host of researchers to utilize this wealth of information for useful purposes like mental illness detection which could help doctors and general practitioners in diagnosing these. Here the authors take a look at some of the Research papers focused more on Depression detection through Social Media-

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