OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model

Ramy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El-Khoury, Ahmad Al Sallab, Hazem Hajj, Nizar Habash, Khaled Shaban, Wassim El-Hajj

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the ``OMAM'' systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D.
Original languageUndefined
Title of host publicationProceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Place of PublicationVancouver, Canada
PublisherAssociation for Computational Linguistics (ACL)
Pages603-610
Number of pages8
DOIs
StatePublished - Aug 1 2017

Cite this

Baly, R., Badaro, G., Hamdi, A., Moukalled, R., Aoun, R., El-Khoury, G., Al Sallab, A., Hajj, H., Habash, N., Shaban, K., & El-Hajj, W. (2017). OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (pp. 603-610). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/S17-2099