Effectiveness of Word Embedding Models in Generating Sub-Emotions with Affinity Propagation Algorithm: A Comparative Analysis
Keywords:
Affinity Propagation (AP), fastText, GloVe model, Machine Learning, Mental illness (MI), Social Media (SM), Word2VecAbstract
Over the past few decades, there has been a rapid increase in the prevalence of Mental Illness (MI) conditions worldwide. Our infrastructure is woefully inadequate to mitigate this problem. In MI, Depression is one of the major causes of suicidal ideation. According to a World Health Organization (WHO) report of 2022, there was a twenty-five per cent increase in MI. It is also estimated that for one lakh people, there should be three MI specialists but approximately there are only 0.75% specialists available and it varies for different countries.
Hence, to alleviate this problem, modern technological conventions like NLP and machine learning should be utilised. In this research, we have implemented and compared Word2Vec and GloVe models such as a) google-news-300, b) GloVe model twitter-25 & twitter-200 and c) fastText, by using them to convert word-to-vector for generating sub-emotions. The developed novel models are also compared to find the number of vocabularies, sub-emotions clusters, Mean (µW) average word per cluster, standard deviation (σW) per cluster and embedded users sample text. The generated sub-emotions can be further used with Machine Learning (ML) and Deep Learning (DL) algorithms to detect Mental illness in social media posts.
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S. Ghosh and T. Anwar, "Depression Intensity Estimation via Social Media: A Deep Learning Approach," in IEEE Transactions on Computational Social Systems, vol. 8, no. 6, pp. 1465-1474, Dec. 2021, doi: 10.1109/TCSS. 2021.3084154.
"DSM-5 Update: Supplement to Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition" (PDF). PsychiatryOnline. American Psychiatric Association Publishing. September 2016.
Al-Ezzi, N. Yahya, N. Kamel, I. Faye, K. Alsaih and E. Gunaseli, "Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity," in IEEE Access, vol. 9, pp. 86899-86913, 2021, doi: 10.1109/ACCESS.2021.3089358.
[online] https://datareportal.com/reports/digital-2022-global-overview-report.
[online] https://www.hootsuite.com/
Paul Ed Ekman and Richard J Davidson. 1994. The nature of emotion: Fundamental questions. New York, NY, US: Oxford University Press.
K. A. Govindasamy and N. Palanichamy, "Depression Detection Using Machine Learning Techniques on Twitter Data," 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 960-966, doi: 10.1109/ICICCS51141.2021.9432203.
S. Tariq et al., "A Novel Co-Training-Based Approach for the Classification of Mental Illnesses Using Social Media Posts," in IEEE Access, vol. 7, pp. 166165-166172, 2019, doi: 10.1109/ACCESS. 2019.2953087.
Uddin, M.Z., Dysthe, K.K., Følstad, A. et al. Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Comput & Applic 34, 721–744 (2022). https://doi.org/10.1007/ s00521-021-06426-4.
M. M. Tadesse, H. Lin, B. Xu and L. Yang, "Detection of Depression-Related Posts in Reddit Social Media Forum," in IEEE Access, vol. 7, pp. 44883-44893, 2019, doi: 10.1109/ACCESS. 2019.2909180.
K. Hemmatirad, H. Bagherzadeh, E. Fazl-Ersi and A. Vahedian, "Detection of Mental Illness Risk on Social Media through Multi-level SVMs," 2020 8th Iranian Joint Congress on Fuzzy and intelligent Systems (CFIS), 2020, pp. 116-120, doi: 10.1109/CFIS49607.2020.9238692.
Olteanu, Alexandra & Kiciman, Emre & Castillo, Carlos. (2018). A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 785-786. 10.1145/ 3159652.3162004.
M. Rizwan, M. F. Mushtaq, U. Akram, A. Mehmood, I. Ashraf and B. Sahelices, "Depression Classification From Tweets Using Small Deep Transfer Learning Language Models," in IEEE Access, vol. 10, pp. 129176-129189, 2022, doi: 10.1109/ACCESS.2022.3223049.
Aragon, Mario Ezra,Lopez-Monroy, Adrian Pastor, Gonzalez-Gurrola, Luis Carlos and Montes-y-Gomez, Manue Montes-y-Gomez, "Detecting Depression in Social Media using Fine-Grained Emotions, “Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", pages 1481-1486, jun- 2019, doi: 10.18653/v1/ N19-1151.
Mohammad Saif M. and Turney Peter D, "Crowdsourcing a Word-Emotion Association Lexicon ", Computational Intelligence, vol. 29, pp. 436-465, 2013
Mohammad Saif M. and Turney Peter D, "Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon", Computational Linguistics, pp. 26-34, 2010.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.
Pennington, J., Socher, R., & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543.
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135-146.
Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315(5814), 972-976.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(Oct), 2825-2830.
Woolf P., Keating A., Burge C., and Michael Y, "Statistics and Probability Primer for Computational Biologists". Massachusetts Institute of Technology, BE 490/ Bio7.91, Spring 2004.
Taylor, J. "An Introduction to Error Analysis". Sausalito, CA: University Science Books, 1982.
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 100-108.
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264-323.
Aragon, Mario Ezra,Lopez-Monroy, Adrian Pastor, Gonzalez-Gurrola, Luis Carlos and Montes-y-Gomez, Manue Montes-y-Gomez, "Detecting Depression in Social Media using Fine-Grained Emotions, “Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", pages 1481-1486, jun- 2019.
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