Implicit and explicit aspect extraction in financial microblogs

Gaillat, Thomas, Stearns, Bernardo, McDermott, Ross, Sridhar, Gopal, Zarrouk, Manel, & Davis, Brian. (2018). Implicit and explicit aspect extraction in financial microblogs. Paper presented at the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 15-20 July.

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.

Funder

Horizon 2020

Publisher

Association for Computational Linguistics

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

Data Science Institute (Conference Papers)