Sarcasm and Sentiment Detection in Arabic language A Hybrid Approach Combining Embeddings and Rule-based Features
Kamel Gaanoun, Imade Benelallam · 2021
This paper presents the ArabicProcessors team’s system designed for sarcasm (subtask 1) and sentiment (subtask 2) detection shared task. We created a hybrid system by combining rule-based features and both static and dynamic embeddings using transformers and deep learning. The system’s architecture is an ensemble of Naive bayes, MarBERT and Mazajak embedding. This process scored an F1-score of 51% on sarcasm and 71% for sentiment detection.