Classification of Mental Health and Emotion of Human from Text using Machine Learning Approaches

Abstract

The use of electronic communication has increased dramatically in recent years, opening up new avenues for mental health research. In an effort to humanize these connections, this study presents a framework for identifying emotions in English language text conversations. As depression is set to become one of the leading causes of death, according to the World Health Organization (WHO), the ability to recognize emotions in text, voice, and facial videos is critical for efficient computing. This research looks at how various machine learning (ML) algorithms perform when it comes to classifying emotions in text. The findings of this work will provide a deeper understanding of the strengths and weaknesses of each algorithm, as well as guidance for future research in the field of emotion recognition in text. This study is an important step towards better-understanding emotions in text conversations and has the potential to contribute to the development of more effective mental health tracking systems and gives a review of recent trends in emotion classification. The outcomes of this study will be of great interest to researchers, practitioners, and stakeholders working in the fields of mental health and emotion recognition in text.