인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
In response to questions or complaints posted by customers on the company"s homepage, the response time is an important measure of customer satisfaction. However, the time it takes for a customer to receive an answer includes a time for the article to be selected by the person in charge of the reply, which limits the shortening. In this study, we developed a model in which a machine, not a person, reads the article, classifies the topic, and delivers it to each person in charge of the article. The article posted on the KEPCO homepage used in this study is a short sentence consisting of an average of 49 words. Due to the scarcity of multi-frequency words, it was found that there is a limit in securing a certain level of topic modeling accuracy in unsupervised machine learning like LDA. To overcome this, we labeled topics and let the machine conduct supervised learning. Although there are limitations in improving accuracy because there are articles containing more than two topics in one article, the classification accuracy is secured up to 84% by using LSTM and Baysian Optimization. The result of this study suggests that topic classification is possible for short-term customer questions in specific fields such as the electric power industry. In addition, it is expected that a model will be developed that can provide optimal reference answers for newly received questions when the topic-labeled questions and answers are fully accumulated.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2019-560-001306925
