Mining the user clusters on Facebook fan pages based on topic and sentiment analysis

Social network websites such as Facebook, Tweeter, and Plurk have become a useful marketing toolkit. Many companies find that it can provide new opportunities on mining customers' opinions and get better understanding of the customers. The aim of the paper is to analyze the sentiment of users' opinions from corporation-run social networks, the Facebook Fan Pages. The goal is to find the topics and the associated sentiment of the topics in a given Fan Page run by a single corporation. Sentiment analysis in previous works was often based on a sentiment dictionary; we follow the traditional approach with the help of some additional rules to improve the performance. Combining the result of topic extraction and sentiment analysis, we try to find the most interested events in the given Fan Page. The results can be used in advanced marketing for the corporation and to satisfy more users.