Abstract
Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.- Anthology ID:
- W19-4604
- Volume:
- Proceedings of the Fourth Arabic Natural Language Processing Workshop
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Wassim El-Hajj, Lamia Hadrich Belguith, Fethi Bougares, Walid Magdy, Imed Zitouni, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 30–39
- Language:
- URL:
- https://aclanthology.org/W19-4604
- DOI:
- 10.18653/v1/W19-4604
- Cite (ACL):
- Hala Mulki, Hatem Haddad, Mourad Gridach, and Ismail Babaoğlu. 2019. Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 30–39, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects (Mulki et al., WANLP 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W19-4604.pdf
- Data
- ASTD, TSAC