Rishibalan B


2024

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KEC_AI_MIRACLE_MAKERS@LT-EDI-2024: Stress Identification in Dravidian Languages using Machine Learning Techniques
Kogilavani Shanmugavadivel | Malliga Subramanian | Monika J | Monishaa S | Rishibalan B
Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion

Identifying an individual where he/she is stressed or not stressed is our shared task topic. we have used several machine learning models for identifying the stress. This paper presents our system submission for the task 1 and 2 for both Tamil and Telugu dataset, focusing on us- ing supervised approaches. For Tamil dataset, we got highest accuracy for the Support Vector Machine model with f1-score of 0.98 and for Telugu dataset, we got highest accuracy for Random Forest algorithm with f1-score of 0.99. By using this model, Stress Identification System will be helpful for an individual to improve their mental health in optimistic manner.