Implicit and Explicit Aspect Extraction in Financial Microblogs

Thomas Gaillat; Bernardo Stearns; Gopal Sridhar; Ross Mcdermott; Manel Zarrouk; Brian Davis

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.

Document type :

Conference papers

Domain :

Humanities and Social Sciences / Library and information sciences

Humanities and Social Sciences / Linguistics

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https://hal.univ-rennes2.fr/hal-02280371
Contributor : Laurence Leroux <>
Submitted on : Tuesday, September 10, 2019 - 3:22:03 PM
Last modification on : Thursday, September 12, 2019 - 1:20:44 AM

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Implicit and Explicit Aspect Extraction in Financial Microblogs

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