잠재적 차량 결함 탐지를 위한 비정형 고객불만 텍스트 데이터 분류

• 영문명: Classification of Unstructured Customer Complaint Text Data for Potential Vehicle Defect Detection
• 발행기관: 한국산업경영시스템학회
• 저자명: 조주현 옥창수 박재일
• 간행물 정보: 『산업경영시스템학회지』제46권 제2호, 72~81쪽, 전체 10쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.06.30

영문 초록

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automo- tive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

목차

1. 서 론

2. 이론적 배경

3. 제안 방법론

4. 결 과

5. 결 론

Acknowledgement

References

키워드

Automotive Industry NHTSA Word2Vec Customer Complaints Unstructured Data