Sentiment analysis dataset in Moroccan dialect: bridging the gap between Arabic and Latin scripted dialect

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 characters. By assembling a diverse range of textual data, we were able to construct a dataset with a range of 19,991 manually labeled texts in Moroccan dialect and also publicly available lists of stop words in Moroccan dialect as a new contribution to Moroccan Arabic resources. In our exploration of sentiment analysis, we undertook a comprehensive study encompassing various machine-learning models to assess their compatibility with our dataset. While our investigation revealed that the highest accuracy of 98.42% was attained through the utilization of the DarijaBert-mix transfer-learning model, we also delved into deep learning models. Notably, our experimentation yielded a commendable accuracy rate of 92% when employing a CNN model. Furthermore, in an effort to affirm the reliability of our dataset, we tested the CNN model using smaller publicly available datasets of Moroccan dialect, with results that proved to be promising and supportive of our findings.