Implicit and Explicit Aspect Extraction in Financial Microblogs
Thomas Gaillat, Bernardo Stearns, Gopal Sridhar, Ross McDermott, Manel Zarrouk, Brian Davis
Abstract
This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.- Anthology ID:
- W18-3108
- Volume:
- Proceedings of the First Workshop on Economics and Natural Language Processing
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Udo Hahn, Véronique Hoste, Ming-Feng Tsai
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 55–61
- Language:
- URL:
- https://aclanthology.org/W18-3108
- DOI:
- 10.18653/v1/W18-3108
- Cite (ACL):
- Thomas Gaillat, Bernardo Stearns, Gopal Sridhar, Ross McDermott, Manel Zarrouk, and Brian Davis. 2018. Implicit and Explicit Aspect Extraction in Financial Microblogs. In Proceedings of the First Workshop on Economics and Natural Language Processing, pages 55–61, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Implicit and Explicit Aspect Extraction in Financial Microblogs (Gaillat et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-3108.pdf