한국도로공사 VOC 데이터를 이용한 토픽 모형 적용 방안

• 영문명: Application of a Topic Model on the Korea Expressway Corporation’s VOC Data
• 발행기관: 한국IT서비스학회
• 저자명: 김지원(Ji Won Kim) 박상민(Sang Min Park) 박성호(Sungho Park) 정하림(Harim Jeong) 윤일수(Ilsoo Yun)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제19권 제6호, 1~13쪽, 전체 13쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2020.12.30

영문 초록

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation’s voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as “other”. Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.

목차

1. 서 론

2. 관련 이론 및 연구 고찰

3. 자료 수집 및 적용 방법론 선정

4. 전통적인 방법 적용

5. 단어 임베딩을 통한 키워드 확장 방법론

6. 결론 및 향후 연구과제

참고문헌

키워드

Voice of Customer(VOC) Text Mining Topic Model Latent Dirichlet Allocation(LDA) Word2Vec