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


KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 1, pp. 17-24, Jan. 2020
https://doi.org/10.3745/KTCCS.2020.9.1.17, Full Text:
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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
, "CNN Architecture Predicting Movie Rating from Audience’s Reviews Written in Korean," KIPS Transactions on Computer and Communication Systems, vol. 9, no. 1, pp. 17-24, 2020. DOI: https://doi.org/10.3745/KTCCS.2020.9.1.17.

[ACM Style]
. 2020. CNN Architecture Predicting Movie Rating from Audience’s Reviews Written in Korean. KIPS Transactions on Computer and Communication Systems, 9, 1, (2020), 17-24. DOI: https://doi.org/10.3745/KTCCS.2020.9.1.17.