ABSTRACT
Our study deals with sentiment analysis in implementing SDG 10 through an illustration that addresses the problems of the LGBT community. The LGBT community includes lesbians, gays, bisexuals, transgender people, and other queer individuals. Using a full archive API search from Twitter, 1.6 million tweets were extracted across five years about LGBTQ communities and used for this research. It uses Machine Learning (ML) and Natural Language Processing (NLP) to perform the standard text preprocessing and identify sentiment in tweets. Textblob calculates sentiments from tweets. Latent Dirichlet Allocation (LDA) topic modeling to identify hidden topics from a collection of tweets that helps to find issues faced by the LGBT community; This model achieved a state-of-the-art performance since no other similar methods in this domain have been carried out which in turn would help government bodies reduce inequalities and implement SDG 10.
