Implicit and Explicit Aspect Extraction in Financial Microblogs
Thomas Gaillat, Bernardo Stearns, Sridhar Gopal, Ross McDermott, Manel Zarrouk, Brian P. Davis · 2018
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.