# Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media
###### 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. short videos; key success factors; Quality Function Deployment; Natural Language Processing; social media reviews +
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Furthermore, research on how to create successful movies is also quite popular, as movie investors need to ensure significant returns on investment. Therefore, the topic of generating high box office revenue has become crucial, attracting many scholars to delve into it. For instance, [PERSON] et al. (2018) used big data to predict movie box office income, while [PERSON] et al. (2018) explored the elements, such as actors, directors, release dates, movie types, script quality, and movie ratings, necessary for a successful movie. [PERSON] et al. (2018) found that social media marketing is crucial for a movie's success. [PERSON] and [PERSON] (2018) argued that for a movie with inadequate promotion, the naming of the movie title can influence its box office performance. [PERSON] et al. (2018) conducted research on global video-on-demand (VOD) and found that popular original soundtracks (OSTs) have a significant impact on movie downloads. Despite recent discussions on factors contributing to movie success, there is a lack of relevant research on the success factors for emerging short video platforms. Therefore, this study will consolidate these movie factors to serve as a candidate set of key success factors for short video platforms. Subsequently, this study proposed the NLP-QFD model, integrating Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), and Quality Function Deployment (QFD) methods. Short news videos from mainstream Western media (CNN) and regional media (Middle East Eye) will be employed as case studies. The collected data contain the video content and audience text reviews. We will use NLP and LDA methods to analyze the text of the video content and the reviews from social media. For comparing the differences in the content of short videos and the differences in audience perspectives after viewing, audio data will be transformed into textual data. In addition to analyzing the content of short videos and audience feedback, 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. To sum up, based on the available literature, there have been no previous studies focusing on the key success factors for short videos. Therefore, this study contributes by  
(1) introducing the NLP-QFD method for identifying the key factors in short videos.