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http://dx.doi.org/10.3745/KTCCS.2020.9.1.17

CNN Architecture Predicting Movie Rating from Audience's Reviews Written in Korean  

Kim, Hyungchan (한국기술교육대학교 컴퓨터공학부)
Oh, Heung-Seon (한국기술교육대학교 컴퓨터공학부)
Kim, Duksu (한국기술교육대학교 컴퓨터공학부)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.9, no.1, 2020 , pp. 17-24 More about this Journal
Abstract
In this paper, we present a movie rating prediction architecture based on a convolutional neural network (CNN). Our prediction architecture extends TextCNN, a popular CNN-based architecture for sentence classification, in three aspects. First, character embeddings are utilized to cover many variants of words since reviews are short and not well-written linguistically. Second, the attention mechanism (i.e., squeeze-and-excitation) is adopted to focus on important features. Third, a scoring function is proposed to convert the output of an activation function to a review score in a certain range (1-10). We evaluated our prediction architecture on a movie review dataset and achieved a low MSE (e.g., 3.3841) compared with an existing method. It showed the superiority of our movie rating prediction architecture.
Keywords
NLP; CNN; Movie Rating; Un-Normalized Text Data;
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