Sentiment Analysis of Arabic Tweets: Detecting Revilement

Abstract

Social media systems play an necessary function in shaping public opinion and reflecting societal sentiments. This study focuses on sentiment analysis in Arabic tweets with a particular focus on offensive or offensive content. The aim of this research is to boost a dependable sentiment evaluation model that can accurately classify Arabic tweets as positive or negative, with a particular focus on identifying offensive language. A multinomial Naive Bayes classifier is trained on pre-processed data to perform sentiment classification. The classifier is fine-tuned to differentiate between positive and negative emotions, with a particular focus on identifying offensive or swearing language. The model is evaluated the usage of a complete set of metrics along with precision, precision, recall, and F1score. Experimental consequences point out promising overall performance of the developed sentiment evaluation model. The model achieved an accuracy of 93%, effectively classifying Arabic tweets into effective and bad sentiments. The precision, recall, and F1-score metrics similarly validate the model's capacity to precisely become aware of revilement and offensive language. These outcomes spotlight the conceivable of the proposed strategy in successfully examining Arabic tweets for sentiment and offensive content, contributing to higher grasp on line behaviors and sentiments in the context of revilement.