Problem definition: Many studies have examined quantitative customer reviews (i.e., star ratings) and found them to be a reliable source of information that has a positive effect on product demand. Yet the effect of qualitative customer reviews (i.e., text reviews) on demand has been less thoroughly studied, and it is not known whether (or how) the sentiment expressed in text reviews moderates the influence of star ratings on product demand. The authors are therefore led to examine how the interplay between review sentiment and star ratings affects product demand. Academic/practical relevance: Consumer perceptions of product quality - and how they are shared via customer reviews - are of extreme relevance to the firm, but it is still not understood how product demand is affected by the quantitative and qualitative aspects of customer reviews. This paper seeks to fill this critical gap in the literature by analyzing star ratings, the sentiment of customer reviews, and their interaction. Methodology: Using 2002-2013 data for the US automobile market, the authors investigate empirically the impact of star ratings and review sentiment on product demand. Thus they estimate an aggregated multinomial choice model after performing a machine learning-based sentiment analysis on the entire corpus of customer reviews included in our sample. Results: The authors find that (i) review sentiment and star ratings both have a decreasingly positive effect on product demand and (ii) the effect (on demand) of their interaction suggests that the two components of reviews are complements. Positive sentiments in text reviews compensate for the tendency of consumers to discount extremely high star ratings, and negative sentiments amplify that discounting tendency. Managerial implications: The firm should pay greater attention to quantitative and qualitative customer reviews so that it can better understand how consumers perceive the quality of its products or services.