Exploring eWOM in online customer reviews: Sentiment analysis at a fine-grained level

Abstract

Customer reviews in social media and electronic commerce Web sites contain valuable electronic word-of-mouth (eWOM) information of products, which facilitates firms’ business strategy and individual consumers’ comparison shopping. Exploring eWOM of products embedded in customer reviews has attracted interest from researchers in various fields. Coarse-grained and context-free sentiment analysis approaches have been used in existing researches, which however often fail to satisfy the firms’ demands of fine-grained extraction of market intelligence from social media. In this study, we propose an original method to explore eWOM of products based on sentiment analysis at fine-grained level from a large volume of online customer reviews. We illustrate a feature-based and context-sensitive sentiment analysis mechanism that can leverage the sheer volume of customer reviews in social media sites. A novel semi-supervised fuzzy product ontology mining algorithm is proposed to extract semantic knowledge from online customer reviews with positive or negative labels. Based on real-world online customer review data set, the proposed method shows remarkable performance improvement over baseline methods at exploring eWOM of product a fine-grained level. With the novel eWOM exploring method, firms can improve their product design and marketing strategies, and potential consumers can make better online purchase decisions.