Analysis of Twitter data to Identify Intuitive Mental Health Well Being
Preeti Gupta, Mansi Gupta, Riya Kansal, Lovely Singh, Mahesh Khotani, V. P. Gupta · 2021 5th International Conference on Information Systems and Computer Networks (ISCON) · 2021
The following paper describes a data-driven predictive approach that has been applied to analyze the mental soundness of the user through their Tweets. Social media platforms have gained immense popularity in the past decade and have active participants which share most of their daily activities on these platforms. Many of the platforms also share much of their personal information along with the data open to the public for research and other purposes. Datasets from different social media platforms are helpful in many fields like sociology and psychology. In this work, the number of tweets in which opinions are highly unstructured and are either positive or negative or sometimes neutral is being used for the analysis. Sentiment Analysis can be supported with several machine learning algorithms to classify the mental health of the user. Simulation results show the enhancement in sentiment analysis of the Twitter data for the intuitive mental wellness of a person. This paper highlights the methodology for mental health analysis for the intuitive well-being of the person. It uses sentiment analysis and its approach to analyze the mental well-being of Twitter users.