A Case Study on the Discrimination of Gender based on the Korean Conversation Text Data

Natural language processing (NLP) technology is leading innovation in various fields along with the development of deep learning. For example, interactive artificial intelligence (AI) in an autonomous vehicle analyzes the user's utterance and provides personalized services such as recommending destinations, performing vehicle control commands, and temperature control. In addition, in the health care field, NLP technology is playing an important role in psychological counseling using voice data and disease diagnosis assistance services. At this time, it is known that applying natural language processing technology is more difficult than other languages because Korean language has unique linguistic characteristics such as freedom of word order, complex investigation, and mother change. In this study, gender is determined by reflecting the specificity of Korean speech data, and the optimal embedding technique and model are explored. To this end, we briefly introduced text embedding techniques such as TF-IDF, Doc2Vec, and BERT, various machine learning models such as decision trees, logistic regression, random forest, SVM, XGBoost, and RNN, and conducted case analysis using large-scale Korean speech data sets provided by AI Hub to compare the performance of data according to various analysis method.