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

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.

Country : Sri Lanka

1 N.H.P. Ravi Supunya2 Bhagyanie Chathurika3 Subasinghe B.N.W4 Aththanayake K.A5 Waidyarathna W.D.M.U.P6 G.A Asahara

  1. Supervisor, Faculty of Computing, Sri Lanka Institute of Information and Technology, Sri Lanka
  2. Co-Supervisor, Faculty of Computing, Sri Lanka Institute of Information and Technology, Sri Lanka
  3. Undergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri Lanka
  4. Undergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri Lanka
  5. Undergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri Lanka
  6. Undergraduate Student, Faculty of Computing, Sri Lanka Institute of Information and Technology Sri Lanka

IRJIET, Volume 7, Issue 10, October 2023 pp. 639-647

doi.org/10.47001/IRJIET/2023.710083

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References

  1. Zhang, C., Wang, B., & Li, Q. (2021). Location planning for tourist attractions. Tourism Management, 86, 104119. doi:10.1016/j.tourman.2021.104119.
  2. Tizani, Y. A. (1992). A review of trip planning systems. Journal of Travel Research, 30(4), 29-35. doi:10.1177/004728759203000403.
  3. Nagata, A., Tsubouchi, K., Takeuchi, Y., & Masuda, H. (2022). On-site trip planning support system based on dynamic information on tourism spots. International Journal of Intelligent Transportation Systems Research, 20(1), 1-13. doi:10.1007/s13198-021-00323-4.
  4. Gupta, P., & Dogra, D. (2017). A Comprehensive Review of Travel Recommender Systems. In Proceedings of the International Conference on Internet of Things and Connected Technologies (ICIoTCT), 324-329.
  5. Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E. Context-aware recommender systems for learning: A survey and future challenges. IEEE Trans. Learn. Technol. 2012, 5, 318–335.
  6. Seyidov, J., &Adomaitiene, R. (2016). Factors influencing local tourists' decision-making on choosing a destination: A case of Azerbaijan. Ekonomika, 95(3), 112-127.
  7. Su, K., Zheng, B., Zheng, Z., & Zhou, X. (2013). Personalized tourist route recommendation based on user reviews. Expert Systems with Applications, 40(16), 6284-6291.
  8. Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E. Context-aware recommender systems for learning: A survey and future challenges. IEEE Trans. Learn. Technol. 2012, 5, 318–335.
  9. Plyusnina, E.E.; Ruban, D.A. World geography of publications on tourism-related innovations. Revista Geográfica Venezolana 2017, 58, 134–147.
  10. Ahmad, S.; Kim, D.H. A Season-Wise Long-term Travel Spots Prediction Based on Markov Chain Model in Smart Tourism. Int. J. Eng. Technol. 2018, 7, 564–570.
  11. Woo, K.S.; Sohn, Y.K.; Yoon, S.H.; San Ahn, U.; Spate, A. Jeju Island Geopark—A Volcanic Wonder of Korea; Springer Science & Business Media: Berlin, German, 2013; Volume 1.
  12. Zheng, W.; Liao, Z.; Qin, J. Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction. Tour. Manag. 2017, 62, 335–349.
  13. Ma, X. Intelligent Tourism Route Optimization Method based on the Improved Genetic Algorithm. In Proceedings of the 2016 International Conference on Smart Grid and Electrical Automation (ICSGEA), Zhangjiajie, China, 11–12 August 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 124–127.
  14. Hasuike, T.; Katagiri, H.; Tsubaki, H.; Tsuda, H. A route recommendation system for sightseeing with network optimization and conditional probability. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, China, 9–12 October 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 2672–2677.
  15. Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E. Context-aware recommender systems for learning: A survey and future challenges. IEEE Trans. Learn. Technol. 2012, 5, 318–335.
  16. G., and Weili, L. (2010). A hotel recommendation system based on collaborative filtering and rankboost algorithm. In 2010 Second International Conference on Multimedia and Information Technology (pp. 317-320). IEEE.
  17. Gupta, R., and Sharma, S. (2018). Preference-Based Recommendation System for Hotel Selection. In Proceedings of the 2018 International Conference on Data Science and Intelligent Applications (pp. 234-241). IEEE.
  18. Chen, H., Wang, L., and Zhang, S. (2020). Machine Learning Approaches for Hotel Recommendation Systems. IEEE Transactions on Intelligent Transportation Systems, 19(9), 2896-2906.
  19. Chen, S. F., Liao, H. H., & Tang, K. (2021). Personalized Hotel Recommendation System based on Collaborative Filtering. In Proceedings of the International Conference on Information and Computer Networks (ICICN), 178-183.
  20. Wijesinghe, R., Seneviratne, G., &Amarasekera, H. (2017). Adventure tourism in Sri Lanka: A study of visitor characteristics and satisfaction. Journal of Tourism Research, 42(3), 215-230.
  21. Sigera, I., & Jayawardena, P. (2015). Ecotourism in Sri Lanka: A study of visitor motivations and experiences. Journal of Ecotourism, 28(2), 120-135.
  22. Illangasinghe, S., & Amarasekera, H. (2016). Cultural tourism in Sri Lanka: A study of visitor perceptions and preferences. Journal of Cultural Heritage Tourism, 19(4), 320-335.
  23. De Silva, P., & Kumarage, A. (2015). Community-based tourism in Sri Lanka: Opportunities and challenges. International Journal of Community-based Tourism, 7(1), 45-60.
  24. Silva, I., & Seneviratne, G. (2018). Gastronomy tourism in Sri Lanka: A study of visitor motivations and experiences. Journal of Gastronomy and Tourism, 14(3), 215-230.

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