Abstract
Although there is by now a considerable amount of research on subjectivity and sentiment analysis on morphologically-rich languages, it is still unclear how lexical information can best be modeled in these languages. To bridge this gap, we build effective models exploiting exclusively gold- and machine-segmented lexical input and successfully employ syntactically motivated feature selection to improve classification. Our best models achieve significantly above the baselines, with 67.93% and 69.37% accuracies for subjectivity and sentiment classification respectively.- Anthology ID:
- W17-1318
- Volume:
- Proceedings of the Third Arabic Natural Language Processing Workshop
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Nizar Habash, Mona Diab, Kareem Darwish, Wassim El-Hajj, Hend Al-Khalifa, Houda Bouamor, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- SEMITIC
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 147–156
- Language:
- URL:
- https://aclanthology.org/W17-1318
- DOI:
- 10.18653/v1/W17-1318
- Cite (ACL):
- Muhammad Abdul-Mageed. 2017. Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space. In Proceedings of the Third Arabic Natural Language Processing Workshop, pages 147–156, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Not All Segments are Created Equal: Syntactically Motivated Sentiment Analysis in Lexical Space (Abdul-Mageed, WANLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-1318.pdf