Experiences of sexual minorities on social media: A study of sentiment analysis and machine learning approaches

Abstract

Nowadays, social media has become a forum for people to express their views on issues such as sexual orientation, legislation, and taxes. Sexual orientation refers to individuals with whom you are attracted and wish to be engaged. In the world, many people are regarded as having different sexual orientations. People categorized as lesbian, gay, bisexual, transgender, queer, and many more (LGBTQ+) have many sexual orientations. Because of the public stigmatization of LGBTQ+ persons, many turn to social media to express themselves, sometimes anonymously. The present study aims to use natural language processing (NLP) and machine learning (ML) approaches to assess the experiences of LGBTQ+ persons. To train the data, the study used lexicon-based sentiment analysis (SA) and six distinct machine classifiers, including logistic regression (LR), support vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF), and gradient boosting (GB). Individuals are positive about LGBTQ concerns, according to the SA results; yet, prejudice and harsh statements against the LGBTQ people persist in many regions where they live, according to the negative sentiment ratings. Furthermore, using LR, SVM, NB, DT, RF, and GB, the ML classifiers attained considerable accuracy values of 97%, 96%, 88%, 100%, 92%, and 91%, respectively. The performance assessment metrics used obtained significant recall and precision values. This study will assist the government, non-governmental organizations, and rights advocacy groups make educated decisions about LGBTQ+ concerns in order to ensure a sustainable future and peaceful coexistence.