Mining product innovation ideas from online reviews

Abstract The importance of online customer reviews to product innovation has been well-recognized in prior literature. Mining online reviews has received extensive attention and efforts. Most existing research on mining online reviews focus on issues such as the impact of reviews on sales, helpfulness of reviews, and customers’ participation in reviews. Few research studies, however, seek to identify and extract innovation ideas for products from online reviews. This type of information is particularly important for product functionality improvement and new feature development from a manufacturer's perspective. Mining product innovation ideas allows a manufacturer to proactively review customer opinion and unlock insights about new functionality and features that the market expects, in order to gain a competitive advantage. In this paper, we propose a deep learning-based approach to identify sentences that contain innovation ideas from online reviews. Specifically, we develop a novel ensemble embedding method to generate semantic and contextual representations of the words in review sentences. The resultant representations in each sentence are then used in a long short-term memory (LSTM) model for innovation-sentence identification. Moreover, we adopt a focal loss function in our model to address the class imbalance problem. We validate our approach with a dataset of 10,000 customer reviews from Amazon. Our model achieves an AUC score of 0.91 and an F1 score of 0.89, outperforming a set of state-of-the-art baseline models in the comparison. Our approach can be extended and applied to many other information extraction tasks.