Evaluating machine learning models efficacy in sentiment analysis for Moroccan Darija: An exploration with MAC dataset

Abstract

Sentiment analysis is an essential technique for classifying and extracting emotions from several data sets. While many basic methods distinguish between negative and positive emotions, advanced approaches may consider additional categories, such as neutral emotions. This becomes very important and difficult when we need to deal with less parsed languages and dialects, such as Moroccan Darija. Our study highlights the nuances of conducting sentiment analysis implementing the MAC dataset, which includes comments in Moroccan Darija. Our main target is to do comparative study and research of the most used machine learning models for Arabic sentiment analysis, especially SVM, NB and KNN. These models have proven their effectiveness in classifying and analyzing emotions in widely studied languages such as English and Arabic. Through this comparative analysis, we aim to realize their effectiveness and adaptability in the Moroccan Darija dialect context.