Govind Anjan* and Alladi Srikar

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

*Corresponding Author: Govind Anjan, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Received: February 16, 2023; Published: March 23, 2023

Abstract

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. Detecting depression is important since it must be observed and treated at an early stage to avoid severe consequences. Our Project aims to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. Given social media postings in English, our system should classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. To build a sophisticated classification system we use different Neural Network models like Long Short Term Memory (LSTM), Bi-Directional LSTM, Convolutional neural network (CNN), Gated recurrent units(GRU). On developing all the models we obtained the highest train accuracy of 98.8 percent and test accuracy of 48 percent.

Keywords: Online Social Media; Depression; Machine Learning; Deep Learning

References

1. Amanat A., et al. “Deep Learning for Depression Detection from Textual Data”. Electronics 5 (2022): 676.
2. A Graves., et al. “Bidirectional lstm networks for improved phoneme classification and recognition”. In Artificial Neural Networks: Biological Inspirations - ICANN 2005, LNCS 3697 (2005): 799-804.
3. Sepp Hochreiter and Jürgen Schmidhuber. “Long short-term memory”. Neural Computation8 (1997): 1735-1780.
4. Jina Kim., et al. “Machine learning for mental health in social media: Bibliometric study”. Journal of Medical Internet Research 3 (2021): e 24870.
5. Alex Krizhevsky., et al. “Imagenet classification with deep convolutional neural networks”. Communications of the ACM6 (2017): 84-90.
6. Mathur P., et al. “Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation”. In: , et al. Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science 12036 (2020).
7. Winda Sari., et al. “Sequential models for text classification using recurrent neural network” (2020).
8. Akkapon Wongkoblap., et al. “Depression detection of twitter posters using deep learning with anaphora resolution: Algorithm development and validation”. JMIR Mental Health (2021).
9. D Goyal., et al. “Emerging Trends and Challenges in Data Science and Big Data Analytics". 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (2020): 1-8.
10. Kumari S., et al. “Analysis of Text Mining Tools in Disease Prediction”. In: Abraham A., Hanne T., Castillo O., Gandhi N., Nogueira Rios T., Hong TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing 1375 (2021).
11. Malik S., et al. “Architecture, Generative Model, and Deep Reinforcement Learning for IoT Applications: Deep Learning Perspective”. In: Pal S., De D., Buyya R. (eds) Artificial Intelligence-based Internet of Things Systems. Internet of Things (Technology, Communications and Computing). Springer, Cham (2022).
12. Tyagi AK., et al. “Blockchain—Internet of Things Applications: Opportunities and Challenges for Industry 4.0 and Society 5.0”. Sensors2 (2023): 947.
13. Akshita Tyagi., et al. “Machine Learning: Past, Present and Future”. Neuroquantology8 (2022).
14. MM Nair., et al. “The Future with Industry 4.0 at the Core of Society 5.0: Open Issues, Future Opportunities and Challenges". 2021 International Conference on Computer Communication and Informatics (ICCCI) (2021): 1-7.
15. Tyagi AK., et al. “Intelligent Automation Systems at the Core of Industry 4.0”. In: Abraham A., Piuri V., Gandhi N., Siarry P., Kaklauskas A., Madureira A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing. Springer, Cham 1351 (2021).
16. Goyal Deepti and Tyagi Amit. “A Look at Top 35 Problems in the Computer Science Field for the Next Decade” (2020).
17. Varsha R., et al. “The Future with Advanced Analytics: A Sequential Analysis of the Disruptive Technology’s Scope”. In: Abraham A., Hanne T., Castillo O., Gandhi N., Nogueira Rios T., Hong TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing 1375 (2021).
18. Akshara Pramod., et al. “Emerging Innovations in the Near Future Using Deep Learning Techniques, Book: Advanced Analytics and Deep Learning Models, Wiley Scrivener” (2022).
19. Madhav AVS and Tyagi AK. “The World with Future Technologies (Post-COVID-19): Open Issues, Challenges, and the Road Ahead”. In: Tyagi A.K., Abraham A., Kaklauskas A. (eds) Intelligent Interactive Multimedia Systems for e-Healthcare Applications. Springer, Singapore (2022).
20. Mishra S and Tyagi AK. “The Role of Machine Learning Techniques in Internet of Things-Based Cloud Applications”. In: Pal S., De D., Buyya R. (eds) Artificial Intelligence-based Internet of Things Systems. Internet of Things (Technology, Communications and Computing). Springer, Cham (2022).
21. Prasad Ashwani., et al. “Human Activity Recognition Using Cell Phone-Based Accelerometer and Convolutional Neural Network". Applied Sciences24 (2021): 12099.
22. Tyagi Amit Kumar., et al. “Security, Privacy Research issues in Various Computing Platforms: A Survey and the Road Ahead". Journal of Information Assurance and Security1 (2021): 1-16.
23. Sharma D and Tyagi AK. “Preserving Privacy in Internet of Things (IoT)-Based Devices”. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems. Springer, Singapore 421 (2023).
24. Nair Meghna Manoj., et al. “Privacy: History, Statistics, Policy, Laws, Preservation and Threat Analysis". Journal of Information Assurance and Security1 (2021): 24-34.
25. Tyagi AK., et al. “P3 Block: Privacy Preserved, Trusted Smart Parking Allotment for Future Vehicles of Tomorrow”. In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science. Springer, Cham 12254 (2020).
26. A Mohan Krishna., et al. “Preserving Privacy in Future Vehicles of Tomorrow”. JCR 19 (2020): 6675-6684.
27. Amit Kumar Tyagi and N Sreenath. “Preserving Location Privacy in Location Based Services against Sybil Attacks”. International Journal of Security and Its Applications12 (2015): 189-210.
28. Amit Kumar Tyagi and N Sreenath. “A Comparative Study on Privacy Preserving Techniques for Location Based Services”. British Journal of Mathematics and Computer Science4 (2015): 1-25.
29. Amit Kumar Tyagi and N Sreenath. “Providing Safe, Secure and Trusted Communication among Vehicular Ad-hoc Networks’ Users: A Vision Paper”. International Journal of Information Technology and Electrical Engineering 1 (2016): 35-44.
30. Tyagi A., et al. “Never Trust Anyone: Trust-Privacy Trade-offs in Vehicular Ad-Hoc Networks”. Journal of Advances in Mathematics and Computer Science6 (2016): 1-23.
31. Amit Kumar Tyagi and Sreenath Niladhuri. “Providing Trust Enabled Services in Vehicular Cloud Computing”. In Proceedings of the International Conference on Informatics and Analytics (ICIA-16). Association for Computing Machinery, New York, NY, USA, Article 3 (2016): 1-10.
32. AK Tyagi., et al. “Trust and Reputation Mechanisms in Vehicular Ad-Hoc Networks: A Systematic Review". Advances in Science, Technology and Engineering Systems Journal1 (2020): 387-402.
33. Tyagi AK and Sreenath N. “Security, Privacy, and Trust Issues in Intelligent Transportation System”. In: Intelligent Transportation Systems: Theory and Practice. Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore (2023).
34. Abhishek B and Tyagi AK. “An Useful Survey on Supervised Machine Learning Algorithms: Comparisons and Classifications”. In: Sengodan, T., Murugappan, M., Misra, S. (eds) Advances in Electrical and Computer Technologies. Lecture Notes in Electrical Engineering 881 (2022).
35. K Sekar and AK Tyagi. "Study of Data Behaviour and Methods for Data Prediction and Analysis". 2022 6^th International Conference on Intelligent Computing and Control Systems (ICICCS) (2022): 1-6.
36. Malik S., et al. “A Novel Feature Extractor Based on the Modified Approach of Histogram of Oriented Gradient”. In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science 12254 (2020).
37. Gillala Rekha., et al. “KDOS - Kernel Density based Over Sampling - A Solution to Skewed Class Distribution”. Journal of Information Assurance and Security (JIAS)2 (2020): 44-52.
38. Gillala Rekha., et al. “Solving Class Imbalance Problem Using Bagging, Boosting Techniques, with and without Noise Filter Method”. International Journal of Hybrid Intelligent Systems2 (2022): 67-76.
39. Gillala Rekha., et al. “A Novel Approach for Solving Skewed Classification Problem using Cluster Based Ensemble Approach”. Mathematical Foundations of Computing1 (2020): 1-9.
40. Gillala Rekha., et al. “CIRUS - Critical Instances removal-based Under-Sampling - A solution for Class Imbalance”. IJHIS2 (2020): 55-66.
41. Amit Kumar Tyagi and Poonam Chahal. “Artificial Intelligence and Machine Learning Algorithms”. Book: Challenges and Applications for Implementing Machine Learning in Computer Vision, IGI Global, (2020).
42. Amit Kumar Tyagi and G Rekha. “Challenges of Applying Deep Learning in Real-World Applications”. Book: Challenges and Applications for Implementing Machine Learning in Computer Vision, IGI Global (2020): 92-118.
43. Gillala Rekha., et al. “An Earth mover's distance-based under sampling approach for handling class-imbalanced data”. International Journal of Intelligent Information and Database Systems2/3/4 (2021).
44. Akshara Pramod., et al. “Machine Learning and Deep Learning: Open Issues and Future Research Directions for Next Ten Years”. Book: Computational Analysis and Understanding of Deep Learning for Medical Care: Principles, Methods, and Applications, 2020, Wiley Scrivener (2020).
45. Tyagi AK and Sreenath N. “Intelligent Transportation System: Past, Present, and Future”. In: Intelligent Transportation Systems: Theory and Practice. Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore (2023).
46. Tyagi AK and Sreenath N. “Artificial Intelligence—Internet of Things-Based Intelligent Transportation System”. In: Intelligent Transportation Systems: Theory and Practice. Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore (2023).
47. Tyagi AK and Sreenath N. “Future Intelligent Vehicles: Open Issues, Critical Challenges, and Research Opportunities”. In: Intelligent Transportation Systems: Theory and Practice. Disruptive Technologies and Digital Transformations for Society 5.0. Springer, Singapore (2023).
48. Madhav AVS and Tyagi AK. “Explainable Artificial Intelligence (XAI): Connecting Artificial Decision-Making and Human Trust in Autonomous Vehicles”. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems 421 (2023).
49. Nair MM and Tyagi AK. “Preserving Privacy Using Blockchain Technology in Autonomous Vehicles”. In: Giri, D., Mandal, J.K., Sakurai, K., De, D. (eds) Proceedings of International Conference on Network Security and Blockchain Technology. ICNSBT 2021. Lecture Notes in Networks and Systems 481 (2022).
50. V S A., et al. “A Review on Recent Trends in Quantum Computation Technology”. In A. Tyagi (Ed.), Handbook of Research on Quantum Computing for Smart Environments (2023): 48-64.

Citation

Citation: Govind Anjan and Alladi Srikar. “Detecting Signs of Depression from Social Media Platforms".Acta Scientific Computer Sciences 5.4 (2023): 74-79.

Copyright

Copyright: © 2023 Govind Anjan and Alladi Srikar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.