This paper presents a way of classifying qualitative online consumer reviews (OCRs) in terms of functional and emotional dimensions and measures the direct and indirect impact of both volume and valence of OCRs on product sales. Utilizing four million online postings across 342 mobile games for thirty months, the authors use text analysis and word classification and identify 74 representative words to describe the various levels of functional OCRs consisting of product quality, product innovativeness, price acceptability, and product simplicity, and emotional OCRs including anger, fear, shame, love, contentment, and happiness. They combine the resulting OCR volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. Results suggest that the volume and valence of aggregated functional OCRs and the valence of aggregated emotional OCRs have the positive effects on sales. The volume and valence of functional OCR subcategories have mixed effects on sales and the link is moderated by the share of emotional OCR subcategories. Further, a sales forecasting model which includes 13 variables of OCR subcategories shows the best predictive validity.