This paper aims to study the role product category plays as a moderating factor in online reviews, by introducing a novel method for product category classification using natural language processing (NLP). The study includes a wide variety of categories, based on a high number of products and number of reviews. The data-set presented includes 1.1 million unique reviews from 4,600 products in 30 different product categories. We find evidence for reviews having an effect on sales, and that this effect interacts with other factors, most notably the product category as well as product popularity. We find that subjectively evaluated products, as well as less popular products see the largest relative effect of WOM. This paper also reveals some evidence of rating biases as 60% of the 1.1 million reviews in our data-set show signs of bimodality. Based on the results we present “the review impact continuum”, a model mapping degree of subjectivity and product popularity enabling managers to assess the expected impact of online consumer reviews for their products.
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