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http://dx.doi.org/10.5351/KJAS.2022.35.1.019

Semantic analysis via application of deep learning using Naver movie review data  

Kim, Sojin (Department of Statistics, Ewha Womans University)
Song, Jongwoo (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.1, 2022 , pp. 19-33 More about this Journal
Abstract
With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.
Keywords
semantic analysis; natural language processing (NLP); LSTM; recurrent neural network (RNN); movie review;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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