Analyzing the Bad-Words in tweets of Twitter users to discover the Mental Health Happiness Index and Feel-Good-Factors

This is the authors' abstract. We don't add key points for this paper.

Happiness is based on individual thoughts, perceived by the things and events around. Social media posts are convenient and useful to gauge the level of happiness using Hedonometrics. There is little possibility of ever calculating happiness accurately because parameters change with person, location, and times. Measurements are made with feelings, words, and expressions. We define the Happiness Index with a ‘bad-words’ set and analyze it using 2.3 million depressive and anti-depressive tweets. The findings support the results from the Latent Dirichlet Allocation (LDA), ARIMA forecast, and Mean Square Errors (MSE) using the moving averages method. Our results correlate well with the World Health Organization reports on confirmed COVID cases and Depression Index measurements.