A soon-to-be published study by University of Iowa researchers finds that companies can find ideas for innovative new products in the consumer review sections on e-commerce websites like Amazon.

The review sections are essentially free focus groups where consumers can leave ideas for new or improved products, says Weiguo Fan, professor of business analytics in the Tippie College of Business and study co-author. Finding those nuggets of consumer wisdom can be a challenge, though. Few companies have the resources to sift through thousands of comments across dozens of e-commerce sites, only a small number of which offer any kind of information that could be used for product innovation.

To find those sentences, Fan and Min Zhang, graduate student in informatics and study co-author, designed an algorithm that looks for certain words and phrases in reviews. The more reviews the algorithm analyzes, the more it can generate semantic and contextual representations of words, learning as it goes and becoming increasingly accurate in identifying comments that offer innovative ideas.

Fan and Zhang used the algorithm to analyze 10,000 randomly selected comments from Amazon.com. The comments had already been manually reviewed by the researchers, who highlighted 243 sentences that suggested innovative ideas. The algorithm flagged 91% of those sentences.

Fan says the study shows that using machine learning to analyze online reviews is an effective and inexpensive tool to improve existing products and develop products that are new and innovative.

Fan’s and Zhang’s study, “Mining Product Innovation Ideas from Online Reviews,” will be published in a forthcoming issue of the Journal of Information Processing and Management.