Unsupervised focus group identification from online product reviews

Technology products and software undergo large pre-release testing which is restricted to selected customers called a focus group. Acquiring feedback from these customers provides valuable information about the potential acceptance of the product in the market. Currently, these groups are formed either by manual or random selection or by out-sourcing, which incurs a substantial cost. However, automatic identification of these customers not only saves human effort in terms of money and time but can also help in obtaining useful feedback from fewer, effective representatives. This paper makes the first attempt at identifying these focus group members automatically through the analysis of online product reviews, posted by various consumers. We propose a novel probabilistic framework for focus group identification in an unsupervised setting and illustrate the efficacy of our approach on a dataset of 1.2 million reviews collected from Amazon.