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Sentiment analysis, the automated process of determining emotions or opinionsexpressed in text, has seen extensive exploration in the field of naturallanguage processing. However, one aspect that has remained underrepresented isthe sentiment analysis of the Moroccan dialect, which boasts a uniquelinguistic landscape and the coexistence of multiple scripts. Previous works insentiment analysis primarily targeted dialects employing Arabic script. Whilethese efforts provided valuable insights, they may not fully capture thecomplexity of Moroccan web content, which features a blend of Arabic and Latinscript. As a result, our study emphasizes the importance of extending sentimentanalysis to encompass the entire spectrum of Moroccan linguistic diversity.Central to our research is the creation of the largest public dataset forMoroccan dialect sentiment analysis that incorporates not only Moroccan dialectwritten in Arabic script but also in Latin letters. By assembling a diverserange of textual data, we were able to construct a dataset with a range of 20000 manually labeled text in Moroccan dialect and also publicly available listsof stop words in Moroccan dialect. To dive into sentiment analysis, weconducted a comparative study on multiple Machine learning models to assesstheir compatibility with our dataset. Experiments were performed using both rawand preprocessed data to show the importance of the preprocessing step. We wereable to achieve 92% accuracy in our model and to further prove its liability wetested our model on smaller publicly available datasets of Moroccan dialect andthe results were favorable.

Sentiment Analysis Dataset in Moroccan Dialect: Bridging the Gap Between Arabic and Latin Scripted dialect