Implicit and Explicit Aspect Extraction in Financial Microblogs

Thomas Gaillat 1 Bernardo Stearns Gopal Sridhar Ross Mcdermott Manel Zarrouk 2 Brian Davis
1 LIDILE - Linguistique, Ingénierie, Didactique des Langues
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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 : Monday, January 20, 2020 - 3:24:05 PM

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UNIV-RENNES2 | UNIV-MONTPELLIER | MIPS | LIRMM | TEXTE | CNRS | UR2-HB | LIDILE | UNIV-RENNES

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

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