Social media sentiment polarity analysis: A novel approach to promote business performance and consumer decision-making

Abstract

In order to have a clear understanding of the market structure as well as the customer trends toward various products, there is a need for every company to collect, monitor, and analyze the user data generated online. In this paper, the online reviews of products from two leading camera manufacturers have been utilized to analyze the user trends. After preprocessing the data, sentiment analysis techniques have been employed to mine the textual content of customers’ opinion and classify them into different polarities according to the theoretical conceptualization of service and performance. The sentiment analysis results using Support Vector Machine provide a high level of accuracy in encapsulating and measuring the sentiments of customers toward the products and services as compared to the other text mining strategies. Further, a competitive analysis technique based on K-means clustering has been implemented to examine the most frequent word which is discussed by the customer. The combination of these two methods provides benefit not only to obtain the best classification but also to help the user focus on the most relevant categories that meet his/her interest.