Advertisement

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)

Abstract

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.

Keywords

Deep learning RNN LSTM SO-PMI 

Notes

Acknowledgement

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).

References

  1. 1.
    Choi, Y.J., Lee, S.J., Jeong, J.: Substitute and complementary relationships between online and offline courses in foreign language education. Korean J. Econ. 25(1), 45–60 (2018)CrossRefGoogle Scholar
  2. 2.
    Kim, S., Lim, K.Y.: The moderating effects of perceived usefulness and self-regulated learning skills on the relationship between participative motivation and learning satisfaction in online continuing education programs. J. Lifelong Learn. Soc. 13(3), 85–107 (2017)CrossRefGoogle Scholar
  3. 3.
    Zhang, P., Moon, H.C.: A study on the effects of O2O commerce characteristics and consumer characteristics on trust, desire and behavioral intention in China. In: Korea Trade Research Association Conference, pp. 107–123 (2015)Google Scholar
  4. 4.
    Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Lee, S.H., Cui, J., Kim, J.W.: Sentiment analysis on movie review through building modified sentiment dictionary by movie genre. J. Intell. Inf. Syst. 22(2), 97–113 (2016)Google Scholar
  6. 6.
    Song, J.S., Lee, S.W.: Automatic construction of positive/negative feature predicate dictionary for polarity classification of product reviews. J. KIISE: Softw. Appl. 38(3), 157–168 (2013)Google Scholar

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

Personalised recommendations