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FinSentiA: Sentiment Analysis in English Financial Microblogs

Authors: 

Thomas Gaillat, Annanda Sousa, Manel Zarrouk, Brian Davis

Publication Type: 
Edited Conference Meeting Proceeding
Abstract: 
FinSentiA: Sentiment Analysis in English Financial Microblogs The objective of this paper is to report on the building of a Sentiment Analysis (SA) system dedicated to financial microblogs in English. The purpose of our work is to build a financial classifier that predicts the sentiment of stock investors in microblog platforms such as StockTwits and Twitter. Our contribution shows that it is possible to conduct such tasks in order to provide fine-grained SA of financial microblogs. We extracted financial entities with relevant contexts and assigned scores on a continuous scale by adopting a deep learning method for the classification. Results show a 0.85 F1-Score on a two-class basis and a 0.62 cosine similarity score.
Conference Name: 
TALN2018
Proceedings: 
In Proceedings of the TALN-CORIA 2018
Digital Object Identifer (DOI): 
XXX
Publication Date: 
14/05/2018
Conference Location: 
France
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes