A Study of BERT-Based Classification Performance of Text-Based Health Counseling Data

Abstract

The entry into a hyper-connected society increases the generalization of communication using SNS. Therefore, research to analyze big data accumulated in SNS and extractmeaningful information is being conducted in various fields. In particular, with the recent development of Deep Learning, the performance is rapidly improving by applying it to the field of Natural Language Processing, which is a language understanding technology to obtain accurate contextual information. In this paper, when a chatbot system is applied to the healthcare domain for counseling about diseases, the performance of NLP integrated withmachine learning for the accurate classification ofmedical subjects from text-based health counseling data becomes important. Among the various algorithms, the performance of Bidirectional Encoder Representations from Transformers was compared with other algorithms of CNN, RNN, LSTM, and GRU. For this purpose, the health counseling data of Naver Q&A service were crawled as a dataset. KoBERT was used to classify medical subjects according to symptoms and the accuracy of classification results was measured. The simulation results show that KoBERTmodel performed high performance by more than 5% and close to 18% as large as the smallest.