Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6838121 | Computers in Human Behavior | 2015 | 7 Pages |
Abstract
In recent years, online social networks have raised the attention of scholars and practitioners to study different aspects of these huge data resources to provide useful insights. In this study, a novel framework for segmentation of fan page users in online social networks is proposed based on the features of post popularity including Likes, Comments, and Polarity. Authors have used different methods including data mining, sentiment analysis and CRM to develop the proposing framework. A case study has been conducted and a set of 100 post's data is extracted from a music band fan page in Facebook to evaluate the framework. Results show that user segmentation led to formation of 4 groups of users having interesting interpretations namely, The Apathetic, Staunch, Ordinary, and Lazy fans. Outcomes of this research are useful for every business owner for improving marketing and customer engagement strategies on social networking sites such as Facebook.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Hamid Khobzi, Babak Teimourpour,