Implicit and explicit aspect extraction in financial microblogs
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Gaillat, Thomas, Stearns, Bernardo, McDermott, Ross, Sridhar, Gopal, Zarrouk, Manel, & Davis, Brian. (2018). Implicit and explicit aspect extraction in financial microblogs. Paper presented at the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 15-20 July.
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.