Stress Recognition in Code-Mixed Social Media Texts using Machine Learning

Abstract

Stress, being a complex emotional state caused by a variety of multiple sources, has the potential for serious effects if left untreated. The primary goal of this research is to select and consider AI models that effectively recognize stress within the complicated domain of social media posts. The significance of this study is not only the categorization of stress but also the interpretation of the sophisticated methods that serve as the basis for these emotional responses. Among the traditional machine learning models, Random Forest, K-Nearest Neighbor, Logistic Regression, Decision Tree, and Support Vector Machine are used. The deep learning model’s LSTM, BiLSTM, and transformer-based models m-BERT, AL-BERT, XLM-RoBERTa, IndicBERT, and Distil-BERT were used. Of those models, LSTM proved to be the best-performing model, with an F1-score of 0.75.