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Results for "Arabic Sentiment Analysis"

Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification

arXiv · Mar 9

This paper presents team SPPU-AASM's hybrid model for Arabic sarcasm and sentiment detection in the WANLP ArSarcasm shared task 2021. The model combines sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. Results show the system achieves an F1-sarcastic score of 0.62 and a F-PN score of 0.715, outperforming existing approaches. Why it matters: The research demonstrates that combining context-free and contextualized representations improves performance in nuanced Arabic NLP tasks like sarcasm and sentiment analysis.

Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST

arXiv · Sep 29

KAUST organized an Arabic Sentiment Analysis Challenge where participants developed ML models to classify tweets as positive, negative, or neutral. The competition used the ASAD dataset with 55K tweets for training, 20K for validation, and 20K for final evaluation. The full dataset of 100K labeled tweets has been released for public use.