Gaillat, T. and Stearns, B. and McDermott, R. and Sridhar, G. and Zarrouk, Manel and Davis, Brian (2018) Implicit and Explicit Aspect Extraction in Financial Microblogs. In: ECONLP 2018 – 1st Workshop on Economics and Natural Language Processing, 20 July 2018, Melbourne, Australia.

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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.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	This work is funded by the SSIX Horizon 2020 project (Grant agreement No 645425)
Keywords:	Implicit; Explicit; Aspect; Extraction; Financial Microblogs;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	13417
Depositing User:	Brian Davis
Date Deposited:	07 Oct 2020 15:00
Refereed:	Yes
Funders:	European Union Horizon 2020 programme
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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