Large Language Models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks across different languages. However, their performance in low-resource languages and dialects, such as Moroccan Arabic (MA), requires further investigation. This study evaluates the performance of ChatGPT-4, different fine-tuned BERT models, FastText as text representation, and traditional machine learning models on MA sentiment analysis. Experiments were done on two open source MA datasets: an X(Twitter) Moroccan Arabic corpus (MAC) and a Moroccan Arabic YouTube corpus (MYC) datasets to assess their capabilities on sentiment text classification. We compare the performance of fully fine-tuned and pre-trained Arabic BERT-based models with ChatGPT-4 in zero-shot settings.

IMORPHĒ is a significantly extended version of MORPHE, a morphology description compiler. MORPHE’s morphology description language is based on two constructs: 1) a morphological form hierarchy, whose nodes relate and differentiate surface forms in terms of the common and distinguishing inflectional features of lexical items; and 2) transformational rules, attached to leaf nodes of the hierarchy, which generate the surface form of an item from the base form stored in the lexicon. While MORPHE’s approach to morphology description is intuitively appealing and was successfully used for generating the morphology of several European languages, its application to Modern Standard Arabic yielded morphological descriptions that were highly complex and redundant. Previous modifications and enhancements attempted to capture more elegantly and concisely different aspects of the complex morphology of Arabic, finding theoretical grounding in Lexeme-Based Morphology. Those extensions are being incorporated in a more flexible and less ad hoc fashion in IMORPHE, which retains the unique features of our previous work but embeds them in an inheritance-based framework in order to achieve even more concise and modular morphology descriptions and greater runtime efficiency, and lays the groundwork for IMORPHE to become an analyzer as well as a generator.