Sentiment Analysis and Homophobia Detection of YouTube Comments

Abstract

Sentiment analysis identifies a graded scale of opinions or emotional responses to a particular subject. Many industries and organisations have been actively researching this area for more than 20 years. The key to understand a user’s behaviour while responding on a social media site is to understand their feelings. In contemporary research, a sentence’s content is evaluated, the emotion predicted, that helps researchers gain an insight on the reaction of an individual towards a social media topic. Here, a sentence’s text data is analysed using several Natural Language Processing techniques before being utilised to categorise this multi-class issue. The detection of homophobia and transphobia in comments on YouTube or other social media sites is second objective of this work. Anger, discomfort, or suspicion against Lesbian, Gay, Bisexual and Transgender people is known as homophobia. It can incite individuals to feel panic, dislike, disrespect, aggression, or wrath. By identifying such occurrences on social media, we can better understand how society works and how people behave. The goal of this work is to analyze social media texts such as comments from YouTube and detect homophobic sentiments using deep learning or machine learning models. In this work 6-layer classification model is used, the F1-Score for sentiment identification using the proposed model in this study was 0.5 on multi-class classification and 0.97 on homophobic/transphobic classification and achieved 1st rank on Homophobic detection in Malayalam language and 4th rank for sentiment analysis in Kannada language. © 2022 Copyright for this paper by its authors.