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Knowledge Extraction from Web Reviews Using Feature Selection Based on Onomatopoeia

  • Fumiaki SaitohEmail author
  • Hikaru Aoki
  • Shohei Ishizu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 528)

Abstract

In the field of Buzz marketing, it is important to extract knowledge to improve products and services from the voice of the customer represented by customer reviews. In Japanese web review sentences, words that co-occur with onomatopoeia it has been confirmed that easy to combine with use sense of product. For sensory evaluation using a products can be easily associated with the satisfaction is obvious, onomatopoeia can be expected to contribute in knowledge extraction on customer satisfaction. A knowledge model for customer satisfaction is constructed by a regression tree that co-occurrence words with onomatopoeias are used as explanatory variables. Effectiveness of the proposed method I was confirmed through the analysis for the customer review data of ramen shop in Tokyo. The knowledge model acquired by our approach contained many words associated with noodles and food, on the other hand the normal regression tree model was included many meaningless words and unrelated words.

Keywords

Text mining Online reviews Voices of the customer (VOC) On-omatopoeia Regression tree 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Industrial and Systems Engineering, College of Science and EngineeringAoyama Gakuin UniversitySagamihara CityJapan

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