Design Automation System for Review Analysis Affiliation for Online Educator Reliability Prediction

  • Kihoon Lee
  • Hyogun Kym
  • Nammee MoonEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)


In this paper, we designed a review analysis automation system to grasp the credibility of the online education matching platform. Web crawling collects and parses reviews and ratings of educators who are atypical data. We will build an emotional dictionary based on the educational field to grasp online educator credibility using collected review data and SO-PMI. We also propose a method for building large - scale learning data based on the emotion dictionary constructed. We proposed a system that provides more reliable review analysis results by measuring the accuracy of emotion dictionary by using deep learning in constructed learning data and evaluation data. Through this, we intend to help judge the credibility of educator in O2O education matching.


Deep learning RNN LSTM SO-PMI 



This research is supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Development Program 2018 (R2018020083).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringHoseo UniversityAsanSouth Korea
  2. 2.Center for Business ArtEwha Womans UniversitySeoulSouth Korea

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