Arabic Sentiment Analysis by Pretrained Ensemble

AbstractThis paper presents the 259 team’s BERT ensemble designed for the NADI 2022 Subtask 2 (sentiment analysis) (Abdul-Mageed et al., 2022). Twitter Sentiment analysis is one of the language processing (NLP) tasks that provides a method to understand the perception and emotions of the public around specific topics. The most common research approach focuses on obtaining the tweet’s sentiment by analyzing its lexical and syntactic features. We used multiple pretrained Arabic-Bert models with a simple average ensembling and then chose the best-performing ensemble on the training dataset and ran it on the development dataset. This system ranked 3rd in Subtask 2 with a Macro-PN-F1-score of 72.49%.