A Comparison of “X” Sentiment Analysis Investigating the Impact of COVID-19 on “Essential Jobs”

Abstract

"This paper investigates transformations of 'Essential Jobs' during the COVID-19 pandemic through sentiment analysis of social media data, specifically focusing on 'X' posts. The study employs a comprehensive methodology consisting of traditional and modern sentiment analysis tools as well as advanced deep learning approaches to examine job-related sentiments across English and German languages. The research demonstrates that the 'Twitter-XLM-RoBERTa' model outperforms other sentiment analysis tools in both base and enhanced implementations, challenging the assumption that deep learning enhancements necessarily improve sentiment analysis performance. The findings indicate significant variations between 'Essential Job' designations. However, the high proportion of 'No-Data' classifications and linguistic variability between English and German datasets suggest methodological limitations." (Authors' abstract, BIBB-Doku)