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dc.contributor.authorGaillat, Thomas
dc.contributor.authorSousa, Annanda
dc.contributor.authorZarrouk, Manel
dc.contributor.authorDavis, Brian
dc.identifier.citationGaillat, Thomas, Sousa, Annanda, Zarrouk, Manel, & Davis, Brian. (2018). FinSentiA: sentiment analysis in English financial microblogs. Paper presented at the 25th French Conference on Natural Language Processing - TALN2018, Rennes, France, 14-18 May, doi: 10.13025/S8XP7Den_IE
dc.description.abstractThe 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.en_IE
dc.publisherNUI Galwayen_IE
dc.subjectSentiment Analysisen_IE
dc.subjectFinancial Entitiesen_IE
dc.subjectContinuous Scaleen_IE
dc.subjectOpinion Mining, Entity levelen_IE
dc.titleFinSentiA: sentiment analysis in English financial microblogsen_IE
dc.typeConference Paperen_IE
dc.local.contactManel Zarrouk. Email:

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