TY  - JOUR
T1  - CNN Architecture Predicting Movie Rating from Audience’s Reviews Written in Korean
AU  - Kim, Hyungchan 
AU  - Oh, Heung-Seon 
AU  - Kim,  Duksu 
JO  - The Transactions of the Korea Information Processing Society
PY  - 2020
DA  - 2020/1/30
DO  - 10.3745/KTCCS.2020.9.1.17
KW  - NLP
KW  - CNN
KW  - Movie Rating
KW  - Un-Normalized Text Data
AB  - 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.