A tlas IA at S em E val-2025 Task 11: F ast T ext-Based Emotion Detection in M oroccan A rabic for Low-Resource Settings (2025)

Abstract: This study addresses multi-label emotion classification in Moroccan Arabic. We developeda lightweight computational approach to detect and categorize emotional content in sevendistinct categories: anger, fear, joy, disgust,sadness, surprise, and neutral. Our findings reveal that our efficient, subword-aware modelachieves 46.44% accuracy on the task, demonstrating the viability of lightweight approachesfor emotion recognition in under-resourcedlanguage variants. The model’s performance,while modest, establishes a baseline for emotion detection in Moroccan Arabic, highlighting both the potential and challenges of applying computationally efficient architectures to dialectal Arabic processing. Our analysis revealsparticular strengths in handling morphologicalvariations and out-of-vocabulary words, thoughchallenges persist in managing code-switchingand subtle emotional distinctions. These results offer valuable insights into the trade-offsbetween speed and accuracy in multilingualemotion detection systems, particularly for low-resource languages.

## introduction emotion detection in natural language processing (nlp) presents significant challenges, particularly for low-resource languages like moroccan darija (moroccan arabic). as digital communication proliferates, understanding emotional nuances becomes crucial for applications in sentiment analysis, social media monitoring, and psychological research @xcite . despite extensive research in emotion classification for well-resourced languages, moroccan darija remains under-explored due to several challenges. its linguistic complexity, marked by high contextdependency and significant deviations from modern standard arabic, complicates nlp tasks. the unique code-switching patterns, blending berber, french, spanish, and arabic, further hinder traditional approaches @xcite . a major bottleneck is the scarcity of annotated datasets, linguistic tools, and computational resources. unlike well-studied languages, darija lacks sentiment lexicons and annotated corpora necessary for emotion detection. additionally, dialectal variability introduces regional and social variations, making generalization difficult. lastly, darija's nuanced emotional expression, deeply tied to prosody, idiomatic speech, and cultural context, remains a challenge for conventional nlp techniques. for these reasons, traditional machine learning approaches often struggle with darija's unique dialectal characteristics, requiring specialized computational techniques to navigate its intricate linguistic landscape. developing robust emotion detection models for darija is not only a technical challenge but also a crucial step toward preserving and understanding the linguistic and emotional nuances of moroccan arabic communication. this paper presents the following contributions: input: " " label: joy moroccan darija presents distinct challenges due to its lack of standardization, extensive dialectal variability, and frequent code-switching with french, berber, and spanish. these factors make traditional nlp techniques less effective, requiring specialized preprocessing and modeling strategies. successfully addressing this task not only advances emotion detection for under-resourced languages but also enhances the accessibility and understanding of sentiment in moroccan arabic social media and digital communication. ## system overview our system leverages fasttext @xcite for multi-label emotion classification, chosen for its efficiency and ability to handle dialectal variations. a key advantage of fasttext is its subwordaware representations, which are particularly beneficial for dialectal arabic, where words exhibit high morphological variability @xcite . additionally, its lightweight architecture makes it well-suited for resource-constrained environments, ensuring scalability for real-world applications. another crucial factor is its robustness to out-of-vocabulary (oov) words, a common issue in informal moroccan darija text due to spelling variations, transliterations, and code-switching. the system follows a structured pipeline, beginning with text preprocessing to clean and normalize input, addressing noise, non-standard spellings, and multilingual elements. the processed text is then used to train the fasttext model, leveraging subword embeddings. finally, during inference, emotion labels are predicted and post-processed to refine outputs, ensuring interpretability and better alignment with the nuances of moroccan darija. this approach effectively tackles the linguistic challenges of the task, making emotion detection in darija more robust and scalable. ## data preparation and model training our study is based on the semeval 2025 task 11 (of semeval-2025 task 11, 2025) dataset @xcite , which focuses on emotion detection in moroccan arabic (darija). the dataset is loaded using the hugging face datasets library, allowing for efficient and reproducible data handling. we specifically work with the moroccan arabic ('ary') subset, ensuring that our approach directly addresses the challenges posed by dialectal variation and code-switching in darija. ## conclusion and future work our emotion detection system for moroccan arabic faces several limitations, including difficulty handling code-switching between arabic, french, and berber, reduced performance on informal text variations, and challenges in capturing contextdependent emotional nuances. the current model architecture also struggles with darija's complex morphology, and the evaluation is limited to accuracy, which does not fully reflect the model's performance. future work should focus on developing emotion lexicons specific to moroccan arabic, integrating darija-specific linguistic features, and exploring hybrid approaches combining fasttext with transformer models like @xcite . cross-lingual transfer learning, larger and more diverse emotion datasets, and the adoption of advanced evaluation metrics will further improve the model's effectiveness and robustness.