Online Unstructured Data Analysis Models with KoBERT and Word2vec: A Study on Sentiment Analysis of Public Opinion in Korean

Online news articles and comments play a vital role in shaping public opinion. Numerous studies have conducted online opinion analyses using these as raw data. Bidirectional encoder representations from transformer (BERT)-based sentiment analysis of public opinion have recently attracted significant attention. However, owing to its limited linguistic versatility and low accuracy in domains with insufficient learning data, the application of BERT to Korean is challenging. Conventional public opinion analysis focuses on term frequency; hence, low-frequency words are likely to be excluded because their importance is underestimated. This study aimed to address these issues and facilitate the analysis of public opinion regarding Korean news articles and comments. We propose a method for analyzing public opinion using word2vec to increase the word-frequency-centered analytical limit in conjunction with KoBERT, which is optimized for Korean language by improving BERT. Naver news articles and comments were analyzed using a sentiment classification model developed on the KoBERT framework. The experiment demonstrated a sentiment classification accuracy of over 90%. Thus, it yields faster and more precise results than conventional methods. Words with a low frequency of occurrence, but high relevance, can be identified using word2vec.