Feature-based opinion mining in financial news: An ontology-driven approach. (August 2017)

• Record Type: Journal Article
• Authors: Salas-Zárate, María del Pilar; Valencia-García, Rafael; Ruiz-Martínez, Antonio; Colomo-Palacios, Ricardo
• Is Part Of: Journal of information science. Volume 43:Number 4(2017)
• Page Start: 458
• Page End: 479
• Publication Date: 2017-08
• Subjects: financial news -- ontology -- opinion mining -- sentiment analysis; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551516645528
• Languages: English
• ISSNs: 0165-5515

Abstract

Financial news plays a significant role with regard to predicting the behaviour of financial markets. However, the exponential growth of financial news on the Web has led to a need for new technologies that automatically collect and categorise large volumes of information in a fast and easy manner. Sentiment analysis, or opinion mining, is the field of study that analyses people's opinions, moods and evaluations using written text on Web platforms. In recent research, a substantial effort has been made to develop sophisticated methods with which to classify sentiments in the financial domain. However, there is a lack of approaches that analyse the positive or negative orientation of each aspect contained in a document. In this respect, we propose a new sentiment analysis method for feature and news polarity classification. The method presented is based on an ontology-driven approach that makes it possible to semantically describe relations between concepts in the financial news domain. The polarity of the features in each document is also calculated by taking into account the words from around the linguistic expression of the feature. These words are obtained by using the 'N_GRAM After', 'N_GRAM Before', 'N_GRAM Around' and 'All_Phrase' methods. The effectiveness of our method has been proved by carrying out a set of experiments on a corpus of 1000 financial news items. Our proposal obtained encouraging results with an accuracy of 66.7% and an F-measure of 64.9% for feature polarity classification and an accuracy of 89.8% and an F-measure of 89.7% for news polarity classification. The experimental results additionally show that the N_GRAM Around method provides the best average results.