Abstract
Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to generate an enormous amount of data. Sentiment analysis, which in general requires annotated data for training, is a well-researched area for widely studied languages such as English. For low-resource languages such as Turkish, there is a lack of such annotated data. We present an annotated Turkish dataset suitable for targeted sentiment analysis. We also propose BERT-based models with different architectures to accomplish the task of targeted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.- Anthology ID:
- 2022.acl-srw.39
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Samuel Louvan, Andrea Madotto, Brielen Madureira
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 467–472
- Language:
- URL:
- https://aclanthology.org/2022.acl-srw.39
- DOI:
- 10.18653/v1/2022.acl-srw.39
- Cite (ACL):
- Mustafa Melih Mutlu and Arzucan Özgür. 2022. A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 467–472, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts (Mutlu & Özgür, ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-srw.39.pdf