Ensemble Language Models for Multilingual Sentiment Analysis

arXiv:2403.06060v1 Announce Type: cross
Abstract: The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get little research due to resource limitations. In this study, we explore sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet …

abstract advanced advancement analysis analyze arxiv cs.cl cs.lg ensemble gap human language language models languages low media multilingual opinions research sentiment sentiment analysis social social media spoken type understanding