@article{ME271583F,
title = "CNN Architecture Predicting Movie Rating from Audience’s Reviews Written in Korean",
journal = "KIPS Transactions on Computer and Communication Systems",
year = "2020",
issn = "2287-5891",
doi = "10.3745/KTCCS.2020.9.1.17",
author = "Hyungchan Kim, Heung-Seon Oh, Duksu Kim",
keywords = "NLP, CNN, Movie Rating, Un-Normalized Text Data",
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."
}