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The Evolution of Language Models Applied to Emotion Analysis of Arabic Tweets
The field of natural language processing (NLP) has witnessed a boom in language representation models with the introduction of pretrained language models that are trained on massive textual data then used to fine-tune downstream NLP tasks. In this paper, we aim to study the evolution of language representation models by analyzing their effect on an under-researched NLP task: emotion analysis; for a low-resource language: Arabic. Most of the studies in the field of affect analysis focused ondoi:10.3390/info12020084 fatcat:dv5raozjibaw3jcrhsqimlrbg4