A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews

The Internet has opened new interesting scenarios in the fields of e-commerce, marketing and on-line transactions. In particular, thanks to mobile technologies, customers can make purchases in a faster and cheaper way than visiting stores, and business companies can increase their sales volume due to a world-wide visibility. Moreover, online trading systems allow customers to gather all the required information about product quality and characteristics, via customer reviews, and make an informed purchase. Due to the fact that these reviews are used to determine the extent of customers acceptance and satisfaction of a product or service, they can affect the future selling performance and market share of a company because they can also be used by companies to determine the success of a product, and predict its demand. As a consequence, tools for efficiently classifying textual customer reviews are becoming a key component of each e-commerce development framework to enable business companies to define the most suitable selling strategies and improve their market capabilities. This paper introduces an innovative framework for efficiently analysing customer sentiments in textual reviews in order to compute their corresponding numerical rating to allow companies to better plan their future business activities. The proposed approach addresses different issues involved in this significant task: the dimension and imprecision of ratings data. As shown in experimental results, the proposed hybrid approach yields better learning performance than other state of the art rating predictors.