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

Title
Design Automation System for Review Analysis Affiliation for Online Educator Reliability Prediction
Authors
Lee K.Kym H.Moon N.
Ewha Authors
김효준
Issue Date
2020
Journal Title
Lecture Notes in Electrical Engineering
ISSN
1876-1100JCR Link
Citation
Lecture Notes in Electrical Engineering vol. 536 LNEE, pp. 303 - 309
Keywords
Deep learningLSTMRNNSO-PMI
Publisher
Springer
Indexed
SCOPUS scopus
Document Type
Conference Paper
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. © 2020, Springer Nature Singapore Pte Ltd.
DOI
10.1007/978-981-13-9341-9_52
ISBN
9789811393402
Appears in Collections:
ETC > ETC
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