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arXiv · 2303.15987

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

Mouad Jbel·Mourad Jabrane·Imad Hafidi·Abdulmutallib Metrane
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Abstract

Sentiment analysis, the automated process of determining emotions or opinions expressed in text, has seen extensive exploration in the field of natural language processing. However, one aspect that has remained underrepresented is the sentiment analysis of the Moroccan dialect, which boasts a unique linguistic landscape and the coexistence of multiple scripts. Previous works in sentiment analysis primarily targeted dialects employing Arabic script. While these efforts provided valuable insights, they may not fully capture the complexity of Moroccan web content, which features a blend of Arabic and Latin script. As a result, our study emphasizes the importance of extending sentiment analysis to encompass the entire spectrum of Moroccan linguistic diversity. Central to our research is the creation of the largest public dataset for Moroccan dialect sentiment analysis that incorporates not only Moroccan dialect written in Arabic script but also in Latin letters. By assembling a diverse range of textual data, we were able to construct a dataset with a range of 20 000 manually labeled text in Moroccan dialect and also publicly available lists of stop words in Moroccan dialect. To dive into sentiment analysis, we conducted a comparative study on multiple Machine learning models to assess their compatibility with our dataset. Experiments were performed using both raw and preprocessed data to show the importance of the preprocessing step. We were able to achieve 92% accuracy in our model and to further prove its liability we tested our model on smaller publicly available datasets of Moroccan dialect and the results were favorable.

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Mouad Jbel, Mourad Jabrane, Imad Hafidi, Abdulmutallib Metrane. 2024-09-12. Sentiment Analysis Dataset in Moroccan Dialect: Bridging the Gap Between Arabic and Latin Scripted dialect. https://doi.org/10.1007/s10579-024-09764-6

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