What role do emotions play for brands in online customer reviews?

Abstract

The field of mining unstructured data has been growing rapidly in business intelligence. An area of application represent online reviews where customers interact socially to share opinions towards brands. Thereby, exchanged emotions play a dominant role, which poses a challenge for brand managers to understand the emotional attitude in customer’s reviews. We develop a text-mining method that extracts information about emotions from customers’ product reviews. We cast the underlying analysis of emotions as a binary classification problem, by using features extracted with the help of a psychologically well-grounded emotion lexicon. Based on this, we identify for various brands, which emotion features are important in reviews that are perceived as helpful (and thus more influential) by customers. We have conducted an empirical investigation with a large, publicly available data set from Amazon. Among other insights, our findings can determine the importance of various emotions for different brands throughout several product categories.