소셜 미디어에서의 토픽 도출을 활용한 구전 효과와 판매 예측

• 영문명: The Prediction of Sales Volume and WoM Effect Based on Topic Extraction from Social Media
• 발행기관: 한국무역연구원
• 저자명: 최재원(Jaewon Choi)
• 간행물 정보: 『무역연구』제16권 제5호, 633~647쪽, 전체 15쪽
• 주제분류: 경제경영 > 무역학
• 파일형태: PDF
• 발행일자: 2020.10.30

영문 초록

Purpose - This study attempts to predict the automobile sales volume and WoM Effect of electronic cars such as the Tesla Model S. Design/Methodology/Approach - This study extracted the data in a new industry, specifically, an electronic car such as the Tesla Model S based on the literature review of the existing media theory: the Media Richness Theory (MRT) and the Media Synchronicity Theory (MST). The unstructured data was used as text data for classifying important topics following the flow of time. Using Latent Dirichlet Allocation (LDA) and Dynamic Topic Modeling (DTM), we deducted the significant topic related to the company performance such as sales volume. Findings - We find out that the text mining approach allowed us to identify the derived indicators for an automobile manufacturer’s market success with centrality measures. Also, this research found evidence of conveyance and convergence for the communication process on the web relative to users’ web comments. Research Implications - Although previous studies used the economic approach, this study established that social media could capitalize on using conveyance and convergence of the communication process (e.g., conveyance vs. convergence) based on MST through theoretical consideration.

목차

Ⅰ. 서론

Ⅱ. 이론적 배경

Ⅲ. 연구 프레임워크와 방법론

Ⅳ. 분석 결과

Ⅴ. 결론

References

키워드

Conveyance Process Media Synchronicity Theory Social Network Text Mining