International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Sentiment Analysis in Social Media Data for Depression Detection System

N.H.P. Ravi SupunyaSupervisor, Faculty of Computing, Sri Lanka Institute of Information and Technology, Sri LankaBhagyanie ChathurikaCo-Supervisor, Faculty of Computing, Sri Lanka Institute of Information and Technology, Sri LankaSubasinghe B.N.WUndergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri LankaAththanayake K.AUndergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri LankaWaidyarathna W.D.M.U.PUndergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri LankaG.A AsaharaUndergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri Lanka

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 639-647

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 07-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.710083

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Abstract

This research introduces depression as a prevalent mental health condition affecting millions of people worldwide. A sentiment analysis framework is developed for Facebook to detect signs of depression within user posts. The system uses NLP (natural language processing) and machine learning algorithms (CNN) to analyze sentiment and classify posts as positive, neutral, or negative. The framework integrates into Facebook's infrastructure, enhancing accuracy and efficiency. It incorporates user-specific contextual information and performs comparative analyses against existing methods and clinical evaluations. The results show the system effectively identifies posts indicative of depressive sentiments with high accuracy and sensitivity. The sentiment analysis framework can be adapted and implemented in various social media platforms, facilitating proactive mental health interventions, and supporting individuals in need. Integrating the system into digital health solutions can contribute to a more comprehensive approach to mental health care, reaching a wider population and providing timely support.

Keywords

Sentiment Analysis, Social Media, Depression, Detection System


Citation of this Article

Mr. N.H.P. Ravi Supunya Swarnakantha, Ms. Bhagyanie Chathurika, Subasinghe B.N.W, Aththanayake K.A, Waidyarathne W.D.M.U.P, G.A Asahara, “Sentiment Analysis in Social Media Data for Depression Detection System” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 639-647, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710083

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