Beyond sentiment classification: A novel approach for utilizing social media data for business intelligence

Abstract

Extracting people's opinions from social media has attracted a large number of studies over the years. This is as a result of the growing popularity of social media. People share their sentiments and opinions via these social media platforms. Therefore, extracting and analyzing these sentiments is beneficial in many ways, for example, business intelligence. However, despite a large number of studies on extracting and analyzing social media data, only a fraction of these studies focuses on its practical application. In this study, we focus on the use of product reviews for identifying whether the reviews signify the intention of purchase or not. Therefore, we propose a novel lexicon-based approach for the classification of product reviews into those that signify the intention of purchase and those that do not signify the intention of purchase. We evaluated our proposed approach using a benchmark dataset based on accuracy, precision, and recall. The experimental results obtained prove the efficiency of our proposed approach to purchase intention identification.