LV Pin, ZHONG Luo, CAI Dunbo, et al., “OHRank: An Algorithm Integrating Mentality and Influence of Opinion Holder for Opinion Mining,” Chinese Journal of Electronics, vol. 22, no. 4, pp. 655-660, 2013,
Citation: LV Pin, ZHONG Luo, CAI Dunbo, et al., “OHRank: An Algorithm Integrating Mentality and Influence of Opinion Holder for Opinion Mining,” Chinese Journal of Electronics, vol. 22, no. 4, pp. 655-660, 2013,

OHRank: An Algorithm Integrating Mentality and Influence of Opinion Holder for Opinion Mining

Funds:  This work is supported by the Young Funds of the National Natural Science Foundation of China (No.61103136);the Excellent Youth Science and Technology Innovation Team Project of the Educational Department of Hubei Province of China (No.T201206) and the Open Foundation of HBIR (No.200906).
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  • Corresponding author: LV Pin, CAI Dunbo, WU Yuntao
  • Received Date: 2012-11-01
  • Rev Recd Date: 2013-01-01
  • Publish Date: 2013-09-25
  • One of the most important tasks in opinion mining is opinion element extraction such as opinion expression, opinion holder and opinion target from a review text. Majority existing methods for opinion mining only concentrate on the review text itself. The mentality and influence of opinion holders is usually overlooked in opinion mining. The isolation of between opinion element extraction methods and mentality and influence of the opinion holder will make it very difficult for users to decide which mined opinions are worth being utilized. This paper introduces a novel concept of opinion network and proposes a PageRank-like algorithm called OHRank. It is first attempt to analyze the valuable opinion from the constructed opinion network by integrating mentality and influence of opinion holder into opinion mining. This proposed approach has been applied to real-world datasets and initial experiments indicate that the mentality and influence of opinion holder and his/her extra activity are helpful for finding valuable opinion and that the OHRank method outperforms benchmark methods that ignore those information.
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