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Implicit and Explicit Aspect Extraction in Financial Microblogs

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.
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https://hal.univ-rennes2.fr/hal-02280371
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Submitted on : Tuesday, September 10, 2019 - 3:22:03 PM
Last modification on : Saturday, March 13, 2021 - 3:22:12 AM
Long-term archiving on: : Friday, February 7, 2020 - 6:44:42 PM

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Thomas Gaillat, Bernardo Stearns, Gopal Sridhar, Ross Mcdermott, Manel Zarrouk, et al.. Implicit and Explicit Aspect Extraction in Financial Microblogs. 1st Workshop on Economics and Natural Language Processing (ECONLP 2018), Jul 2018, Melbourne, Australia. pp.55-61, ⟨10.5281/zenodo.1326536⟩. ⟨hal-02280371⟩

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