Fine-grained, aspect-based sentiment analysis on economic and financial lexicon

Extracting sentiment from news text, social media and blogs has recently gained increasing interest in economics and finance. Despite many successful applications of sentiment analysis (SA) in these domains, the range of semantic techniques employed is still limited and predominantly focused on the detection of sentiment at a coarse-grained level. This paper proposes a novel methodology for Fine-Grained Aspect-based Sentiment (FiGAS) analysis. The aim is to identify the sentiment associated with specific topics of interest in each sentence of a document and to assign real-valued polarity scores between -1 and +1 to those topics. The proposed approach is unsupervised and customised to the economic and financial domains by using a specialised lexicon provided by us along with the FiGAS source code. Our lexicon-based SA approach relies on a detailed set of semantic polarity rules that allow understanding of the origin of sentiment - in the spirit of the recent trend on Interpretable AI. We provide an in-depth comparison of the performance of the FiGAS algorithm with other popular lexicon-based SA approaches in predicting a humanly annotated dataset in the economic and financial domains. Our results indicate that FiGAS statistically outperforms the other methods by providing a sentiment score that is closer to those of human annotators. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).