Abstract

Due to the spread of smart devices and social media, and the increase in product purchases online, many companies are trying to understand consumers' consumption patterns and thoughts. Accordingly, the need to understand consumers' emotions by collecting reviews including consumers' opinions on products or services online is increasing, and related research is being conducted by domestic and foreign companies and research institutes. However, most of the studies are still focused on data expressed in English, and many studies and results on sentiment analysis as a lexicon or machine learning approach for English text have been published. On the other hand, the Korean language has relatively low accuracy due to the complexity of Korean and the lack of labeling data for deep learning. To improve these problems, this study utilized a hybrid approach system that improves the accuracy of sentiment analysis by utilizing the advantages of deep learning and sentiment dictionary techniques for Korean online reviews. Through this, it was confirmed how much the indicators such as accuracy, precision, and recall improved. The results of this study are expected to help companies automatically analyze and utilize a large amount of online reviews in the future