Fast-Track Product Evaluation From Text Reviews in M-Commerce: A Fuzzy VIKOR and Text Classification Approach

Abstract

The popularity of mobile commerce has offered many new challenges for investigating public sentiments. With an uncountable number of stores and products available on the marketplace, customers heavily relied on the comments or reviews posted by others to support their buying decisions. For the online retailer's side, these text reviews are valuable resources to understand the latest customer expectations and devise a better product plan for launching suitable products to customers. Sentiment analysis is then developed for the evaluation of a significant amount of text data by searching the sentiment words. Nevertheless, different writers may have different perceptions on the sentiment words, and hence, this inconsistency would be amplified. In this connection, a novel approach to obtain public sentiment by combining the topic modeling, fuzzy set, and multi-criteria decision-making approaches is proposed. The uncertainty of different perceptions on the sentiment words is remedied by fuzzy-set.