Identifying influential reviewers for word-of-mouth marketing

Yung-Ming Li*, Chia Hao Lin, Cheng Yang Lai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

95 Scopus citations

Abstract

The key to word-of-mouth marketing is to discover the potential influential nodes for efficiently spreading product impressions. In this paper, a framework combined with mining techniques, a modified PMI measure, and an adaptive RFM model is proposed to evaluate the influential power of online reviewers. An artificial neural network is adopted to identify the target reviewers and a well-developed trust mechanism is utilized for effectiveness evaluation. This proposed framework is verified by the data collected from Epinions.com, one of the most popular online product review websites. The experimental results show that the proposed model could accurately identify which reviewers to select to become the influential nodes. This proposed approach can be exploited in effectively carrying out online word-of-mouth marketing, which can save a lot of resources in finding customers.

Original languageEnglish
Pages (from-to)294-304
Number of pages11
JournalElectronic Commerce Research and Applications
Volume9
Issue number4
DOIs
StatePublished - 1 Jul 2010

Keywords

  • Opinion mining
  • PMI
  • RFM
  • Social network
  • Trust
  • Word-of-mouth marketing

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