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Gaillat, T., Stearns, B., McDermott, R., Sridhar, G., 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 |
| 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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