Consumer perceptions are important components of brand equity and therefore marketing strategy. Segmenting these perceptions into attributes such as eco-friendliness, nutrition, and luxury enable a fine-grained understanding of the brand’s strengths and weakness. Traditional approaches towards monitoring such perceptions (e.g., surveys) are costly and time-consuming, and their results may quickly become outdated. Extant data mining methods are not suitable for this goal, and generally require extensive hand-annotated data or context customization, which leads to many of the same limitations as direct elicitation. Here, we investigate a novel, general, and fully automated method for inferring attribute-specific brand perception ratings by mining the brand’s social connections on Twitter.

Using a set of over 200 brands and three perceptual attributes, we compare the method’s automatic ratings estimates with directly-elicited survey data, finding a consistently strong correlation. The approach provides a reliable, flexible, and scalable method for monitoring brand perceptions, and offers a foundation for future advances in understanding brand-consumer social media relationships.


  author =       {Aron Culotta and Jennifer Cutler},
  title =        {Mining brand perceptions from Twitter social networks},
  journal = {Marketing Science},
  year =         2016,
  note = {(to appear)}