Studies have shown that perceptual maps derived from online consumer-generated data are effective for depicting market structure such as demonstrating positioning of competitive brands. However, most text mining algorithms would require manual reading to merge extracted product features with synonyms. In response, Topic modeling is introduced to group synonyms together under a topic automatically, leading to convenient and accurate evaluation of brands based on consumers’ online reviews. To ensure the feasibility of employing Topic modeling in assessing competitive brands, we developed a unique and novel framework named WVAP (Weights from Valid Posterior Probability) based on Scree plot technique. WVAP can filter the noises in posterior distribution obtained from Topic modeling, and improve accuracy in brand evaluation. A case study exploring online reviews of mobile phones is conducted. We extract topics to reflect the features of the cell phones with a qualified validity. In addition to perceptual maps derived by multi-dimensional scaling (MDS) for product positioning, we also rank these products by TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) so as to visualize the market structure from different perspectives. Our case study of cell phones shows that the proposed framework is effective in mining online reviews and providing insights into the competitive landscape. 
Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches