Voice of the Professional: Professional generated content (PGC) serves as a vital and reliable online source that provides large-scale information about various aspects of brands and products. This study focuses on acquiring product-level competitive intelligence from large-scale PGCs. Specifically, we aim to simultaneously identify competitive relationships among products, extract representative topics shared by competing products, and estimate content preferences.

To this end, we present a topic model that jointly leverages textual content and their associated product tags in PGCs. Owing to large-scale and lengthy PGCs, we propose a collapsed variational Bayesian inference algorithm to improve the model learning. We analyze over 100,000 PGCs and 3,000 associated products for empirical application in automobiles.

Experimental results show that the proposed approach can accurately analyze market competition. Our findings have significant implications for product managers, enabling them to identify competitors, assess experts’ opinions on their products and competitors, and select high-quality content creators to improve promotions. Newswise/SP