Autores
Kawo Lemlem Eyob
Zamir Muhammad Tayyab
Sidorov Grigori
Batyrshin Ildar
Título Stress Recognition in Code-Mixed Social Media Texts using Machine Learning
Tipo Revista
Sub-tipo CONACYT
Descripción International Journal of Combinatorial Optimization Problems and Informatics
Resumen 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 transformerbased models m -BERT, AL -BERT, XLM-RoBERTa, IndicBERT, and Distil -BERT were used. Of those models, LSTM proved to be F1 -score of 0.75
Observaciones DOI 10.61467/2007.1558.2024.v15i1.430
Lugar Juitepén, Morelos
País Mexico
No. de páginas 32-38
Vol. / Cap. v. 15 no. 1
Inicio 2024-01-01
Fin
ISBN/ISSN