journal article·2024-06-04Open access

Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media

Hsin-Cheng Wu, Wu-Der Jeng, Long-Sheng Chen, Cheng-Chin Ho

Applied Sciences· Volume 14· Issue 11

Abstract

In the transition from television to mobile devices, short videos have emerged as the primary content format, possessing tremendous potential in various fields such as marketing, promotion, education, advertising, and so on. However, from the available literature, there is a lack of studies investigating the elements necessary for the success of short videos, specifically regarding what factors need to be considered during production to increase viewership. Therefore, this study proposed the NLP-QFD model, integrating Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), and Quality Function Deployment (QFD) methods. Real short videos from mainstream Western media (CNN) and regional media (Middle East Eye) will be employed as case studies. In addition to analyzing the content of short videos and audiences’ reviews, we will utilize the NLP-QFD model to identify the key success factors (KSFs) of short videos, providing guidance for future short video creators, especially for small-scale businesses, to produce successful short videos and expand their influence through social media. The results indicate that the success factors for short videos include the movie title, promotion, reviews, and social media. For large enterprises, endorsements by famous individuals are crucial, while music and shooting are key elements for the success of short videos for small businesses.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Digital Marketing and Social Media · Customer Service Quality and Loyalty · Sentiment Analysis and Opinion Mining

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan

Department of Industrial Engineering and Management, Minghsin University of Science and Technology, Hsinchu 304001, Taiwan

Funding

National Science and Technology Council, Taiwan

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