Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter

Highlights：

• This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users' temporal activity

• Time series of mentions made by individual users to each company's Twitter account are aggregated to obtain collective activity data for the companies

• Classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, are used to extract collective temporal behavior patterns and models of the dynamics of customers over time

• The methodology is validated in a case study from the wine market using data gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley)

• The findings presented show that the proposed methodology provides winery companies with new collective knowledge that can be very valuable