Application of NLP and Machine Learning for Mental Health Improvement
Trinayan Borah1, S. Ganesh Kumar2

1Trinayan Borah*, M. Tech Student, Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India. 
2S. Ganesh Kumar, Professor, Department of Data Science and Business Systems, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India. 
Manuscript received on 11 June 2022. | Revised Manuscript received on 02 July 2022. | Manuscript published on 30 August 2022. | PP: 47-52 | Volume-11 Issue-6, August 2022. | Retrieval Number: 100.1/ijeat.F36570811622 | DOI: 10.35940/ijeat.F3657.0811622
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Abstract: Humans’ most powerful tool is their mental wellness. Individuals’ well-being can be impacted by poor mental health. This paper focuses on a smart technical solution to the problem of mental health issues detection related to the stress, sadness, depression, anxiety etc. which if not handled efficiently may further lead to a severe problem. The paper deals with the designing of an automated smart system using social media posts, that will help mental health experts to successfully identify and understand about the mental health condition of social media users. That can be done based on text analysis of rich social media resources such as Reddit, Twitter posts. The implementation of the system is done using Natural Language Processing (NLP) methods, machine learning and deep learning algorithms. The models are trained using a prepared dataset of social media postings. With this automated system the mental health experts can able to detect the stress or some other emotions of social media uses in a very earlier as well as faster way. The proposed system can predict five emotional categories: ‘Happy’, ‘Angry’, ‘Surprise’, ‘Sad’, ‘Fear’ based on machine learning (Logistic Regression, Random Forest, SVM), deep learning Long Short-Term Memory (LSTM) and BERT transfer learning algorithms. All the applied algorithms are evaluated using confusion matrix, the highest accuracy and f1 score achieved is more than 90%, which is better than the existing human emotion detection systems. 
Keywords: Natural Language Processing (NLP), Text-Analysis, Machine Learning, Deep Learning, Transfer Learning, Text2emotion, Social Media Posts, LSTM, BERT
Scope of the Article: Machine Learning