FinSentiA: sentiment analysis in English financial microblogs
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Gaillat, 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/S8XP7D
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.