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

Kuan Cheng Lin, Shih Hung Wu, Liang Pu Chen, Tsun Ku, Gwo Dong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014
EditorsElisa Bertino, Bhavani Thuraisingham, Ling Liu, James Joshi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages627-632
Number of pages6
ISBN (Electronic)9781479958801
DOIs
StatePublished - 27 Feb 2014
Event15th IEEE International Conference on Information Reuse and Integration, IEEE IRI 2014 - San Francisco, United States
Duration: 13 Aug 201415 Aug 2014

Publication series

NameProceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014

Conference

Conference15th IEEE International Conference on Information Reuse and Integration, IEEE IRI 2014
Country/TerritoryUnited States
CitySan Francisco
Period13/08/1415/08/14

Keywords

  • Facebook fan pages
  • Sentiment analysis
  • Social network
  • Topic mining
  • User cluster mining

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