Korean Language NLP Model Based Emotional Analysis of LGBTQ Social Media Communities

Abstract

Sexual minorities are increasingly gaining social visibility and legal rights guarantees at the constitutional level across much of the world, from South America, the United States, and Europe to Japan, Taiwan, and Thailand. At the same time, the COVID-19 pandemic has brought on significant mental health challenges for the public due to accompanying social and economic impact and measures, most of them adverse. Given pre-existing studies highlighting the minority demographic's vulnerability to depression and other mental health symptoms, and the increasing availability of accessible NLP tools, datasets, and models, this paper uses an emotional classification model to analyze emotional trends in queer communities on social media. Using KoBERT with a pre-labelled dataset containing some forty thousand scraped social media posts labelled with emotions, patterns of emotional expression on Twitter in the queer community is revealed. Resulting data provided a validation of the viability of this method of analyzing trends in negative and positive emotional expression as well as the impact COVID-19 had on online queer communities in early 2020 but revealed limitations.