Evaluating Student Depression Detection from Social Media Post Using Multinomial Naïve Bayes Text Vectorization and Randomizedsearchcv

Purpose - The complexities of recognizing student depression make it a promising area for exploring the effectiveness of various text vectorization techniques such as `CountVectorizer`,

`TfidfVectorizer`, and `HashingVectorizer` in machine learning applications. These techniques can help identify patterns and symptoms by analyzing large datasets like social media activity which may reveal indicators of depression that traditional diagnostic methods might miss.

Method - In recognizing student depression through machine learning, the methodology follows the Knowledge Discovery in Databases (KDD) process which guides each stage of data handling and analysis.

Results - Based on the result of the evaluation of different text vectorization techniques, the `CountVectorizer` achieved the highest classification accuracy at 87%, ranking it first. `TfidfVectorizer` follows closely with an accuracy of 86%, ranking second. Lastly, `HashingVectorizer` achieved a lower accuracy of 77%, placing it third in performance.

Conclusion – The study reaffirms that traditional vectorization methods like `CountVectorizer` and `TfidfVectorizer` remain highly effective for text classification tasks and that method selection should be guided by the specific needs of the application and characteristics of the data.

Recommendation - Future work will center on combining these techniques or probing more advanced embedding methods which better capture deeper semantic relations. The generalization of such vectorizers on other datasets and domains will be further illustrated in detail by their robustness and performance.

Depression, Text Vectorization, Count Vectorizer, Tfidf Vectorizer, Hashing Vectorizer

1. Acheampong, F. A., Wenyu, C., & Nunoo‐Mensah, H. (2020). Text‐based emotion detection: Advances, challenges, and opportunities. Engineering Reports, 2(7), e12189. [Google Scholar] [Crossref]

2. Al Asad, N., Pranto, M. A. M., Afreen, S., & Islam, M. M. (2019, November). Depression detection by analyzing social media posts of user. In 2019 IEEE international conference on signal processing, information, communication & systems (SPICSCON) (pp. 13-17). IEEE. [Google Scholar] [Crossref]

3. AlSagri H. S. & Ykhlef M. (2020). Machine learning-based approach for depression detection in twitter using content and activity features. IEICE Transactions on Information and Systems, 103(8), 1825-1832. [Google Scholar] [Crossref]

4. Alzahrani, A., Alosaimi, N., Alzahrani, M., Aljohani, N., & Alshehri, S. (2024). Prevalence and associated factors of depression among university students in Saudi Arabia: A cross-sectional study. Frontiers in Public Health, 12, 1441695. https://doi.org/10.3389/fpubh.2024.144169 5 [Google Scholar] [Crossref]

5. Ashraf A., Gunawan T. S., Riza B. S., Haryanto E. V., & Janin Z. (2020). On the review of image and video-based depression detection using machine learning. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 19(3), 1677-1684 [Google Scholar] [Crossref]

6. Chatterjee, R., Gupta, R. K., & Gupta, B. (2021). Depression detection from social media posts using multinomial naive theorem. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012095). IOP Publishing. [Google Scholar] [Crossref]

7. Chen S., Webb G. I., Liu L., & Ma X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361. [Google Scholar] [Crossref]

8. Chiong R., Budhi G. S., Dhakal S., & Chiong F. (2021). A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Computers in Biology and Medicine, 135, 104499. [Google Scholar] [Crossref]

9. Dabhane S. & Chawan, P. M. (2020). Depression detection on social media using machine learning techniques: a survey. Int. Res. J. Eng. Technol, 7(11), 97-100. [Google Scholar] [Crossref]

10. Daiv K., Lachake M., Jagtap P., Dhariwal S., & Gutte V. (2020). An approach to detect fake reviews based on logistic regression using review-centric features. Int. Res. J. Eng. Technol.(IRJET), 7(06), 2107-2112. [Google Scholar] [Crossref]

11. Deshpande M. & Rao V. (2017, December). Depression detection using emotion artificial intelligence. In 2017 international conference on intelligent sustainable systems (iciss) (pp. 858-862). IEEE. [Google Scholar] [Crossref]

12. Fayyad U., Piatetsky-Shapiro G., & Smyth P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17(3), 37. https://doi.org/10.1609/aimag.v17i3.1230 Govindasamy K. A. & Palanichamy N. (2021, May). [Google Scholar] [Crossref]

13. Depression detection using machine learning techniques on twitter data. In 2021 5th international conference on intelligent computing and control systems (ICICCS) (pp. 960-966). IEEE. [Google Scholar] [Crossref]

14. Havergal, C. (2024, February 22). Number of UK students reporting mental health difficulties triples. Times Higher Education. Retrieved from https://www.timeshighereducation.com/ne ws/number-uk-students-reporting-mental- health-difficulties-triples [Google Scholar] [Crossref]

15. Islam M. R., Kabir M. A., Ahmed A., Kamal A. R. M., Wang H., & Ulhaq A. (2018). Depression detection from social network data using machine learning techniques. Health information science and systems, 6, 1-12. [Google Scholar] [Crossref]

16. Joshi M. L. & Kanoongo N. (2022). Depression detection using emotional artificial intelligence and machine learning: A closer review. Materials Today: Proceedings, 58, 217-226. [Google Scholar] [Crossref]

17. Kibriya A. M., Frank E., Pfahringer B., & Holmes G. (2005). Multinomial naive bayes for text categorization revisited. In AI 2004: [Google Scholar] [Crossref]

18. Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004. Proceedings 17 (pp. 488-499). Springer Berlin Heidelberg [Google Scholar] [Crossref]

19. Li X., Zhang X., Zhu J., Mao W., Sun S., Wang Z., Xia C., & Hu B. (2019). Depression recognition using machine learning methods with different feature generation strategies. Artificial intelligence in medicine, 99, 101696. [Google Scholar] [Crossref]

20. Naidu G., Zuva T., & Sibanda E. M. (2023, April). A review of evaluation metrics in machine learning algorithms. In Computer Science On-line Conference (pp. 15-25). Cham: Springer International Publishing. [Google Scholar] [Crossref]

21. Paykel, E. S. (2008). Basic concepts of depression. Dialogues in clinical neuroscience, 10(3), 279-289. [Google Scholar] [Crossref]

22. Rish I. (2001, August). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence (Vol. 3, No. 22, pp. 41-46). [Google Scholar] [Crossref]

23. Roshan R., Bhacho I. A., & Zai S. (2023). Comparative Analysis of TF–IDF and Hashing Vectorizer for Fake News Detection in Sindhi: A Machine Learning and Deep Learning Approach. Engineering Proceedings, 46(1), 5. [Google Scholar] [Crossref]

24. Sudha K., Sreemathi S., Nathiya B., & RahiniPriya D. (2020). Depression detection using machine learning. In International Journal of Research and Advanced Development. on AICTE Sponsored International Conference on Data Science & Big Data Analytics for Sustainability. [Google Scholar] [Crossref]

• "Towards GDP to GEP-Centric Model: A Proposed GEP Index Framework and Its Application in Haryana"
• Physicochemical Analysis of Petroleum Products in Selected Depots in Calabar Metropolis Cross River State and The Effects on Motor Engine
• Impact of Employee Welfare Measures on Workforce Well-Being in SBI: Insights from the Public Banking Sector
• Problem of Small-Scale Farmers in Agricutlure Sector in Tirunelveli Taluk
• Globalization, Economic Development, and Ecological Footprint in Tunisia: A QARDL Approach