Abstract
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.
Highlights
Sentiment Analysis (SA) in the financial domain has shown a growing interest in recent years
The originality of our work is to evaluate different aspect category identification approaches based on a predefined taxonomy of stock-investment aspects
The distinction between implicit and explicit aspect messages shows that explicit aspects are well classified while implicit aspects are only correctly handled in about 35% of cases
Summary
Sentiment Analysis (SA) in the financial domain has shown a growing interest in recent years. Acquiring an insight into the public opinion of relevant and valuable economic signals can give a competitive edge and allow more informed investment decisions to be executed Microblog platforms such as Twitter and StockTwits, are central to determining these economic signals (Bollen et al, 2011; Zhang et al, 2011). As stated in SemEval-2015, the problem in Aspect-based SA can be divided into three subtasks, i.e. aspect category identification, Opinion Target Expression (OTE) extraction and sentiment polarity assignment (Pontiki et al, 2015). The originality of our work is to evaluate different aspect category identification approaches based on a predefined taxonomy of stock-investment aspects.
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