Mine Your Own Business: Market-Structure Surveillance Through Text Mining

A picture is worth a thousand words: Translating product reviews into a product positioning map

“…Other studies intended to be comprehensive in capturing product features (Decker and Trusov 2010; Lee and Bradlow 2011), but were based on aggregate product "pros and cons" counts by brand instead of full product reviews. To make the most of common product reviews, our proposed study takes the full text in these reviews to identify and taxonomize the product features (Netzer et al 2012;Tirunillai and Tellis 2014). We also apply a multiple-layer hierarchy of the features with descriptive terms nested into grand topics (e.g., "vegetative" in the wine category) and…”

“…With such individual differences in mind, we propose a framework to translate online product reviews into a product positioning map that parses out the perceptions and preferences for competing brands from the individual characteristics (acuity, biases, and writing styles) embedded in these reviews. Instead of aggregating the product reviews into a brand-by-attribute table, as is typically done in the literature (Aggarwal, Vaidyanathan, and Venkatesh 2009;Lee and Bradlow 2011;Netzer, Feldman, Goldenberg, and Fresko 2012), we look into each review directly as the basic unit for our analysis, first generating a lexical taxonomy for the product category using ontology-learning-based text mining, and then analyzing the subsequent coding of each product review using psychometric mapping techniques.…”

What makes consumers unsatisfied with your products: Review analysis at a fine-grained level

“…Utilizing online reviews, researchers extracted product names and product features using CRFs (Netzer et al, 2012). With the proposed approaches, two applications were described to compare products.…”

Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches

“…Through text mining, researchers can discern patterns and trends hidden in the abundant product reviews (Lee and Bradlow 2011;Netzer et al 2012). By ''listening in'' online voice, researchers have made significant improvement in tapping into user-generated content to extract product features and build competitive market landscape.…”

“…For instance, Lee and Bradlow (2011) developed a text-mining algorithm for online product reviews. And Netzer et al (2012) proposed a hybrid text-mining and semantic network analysis tool for market-structure surveillance. However, based on http://dx.doi.org/10.1016/j.elerap.2014.11.004 1567-4223/Ó 2014 Elsevier B.V. All rights reserved.…”