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User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments
- 1 School of Future Science and Engineering, Soochow University, Suzhou, 215222, China
* Author to whom correspondence should be addressed.
Abstract
With the rapid advancement of AI, AI-generated video tools have become essential in digital content creation. However, there are significant differences in user acceptance and satisfaction with these tools. This study aims to explore the underlying reasons for these differences by analyzing user behavior, satisfaction levels, and preference characteristics through a questionnaire survey and online review data. Ridge regression analysis identified usage frequency and data security concerns as key factors influencing satisfaction, with familiarity and education level also playing significant roles. Based on these factors, K-means clustering categorized users into three groups: occasional users with high satisfaction, functionality-focused users with the highest satisfaction, and frequent users with lower satisfaction due to data security concerns. Sentiment analysis and LDA topic modeling of online reviews for five AI video tools-Animoto, Invideo, Lumen5, Pictory, and Synthesia-revealed differences in user evaluations. The findings indicate that Lumen5 excels in social media content creation, while Pictory has a weaker user experience with more negative feedback. The study concludes that developers should focus on increasing tool usage frequency and enhancing data security to improve user satisfaction. Moreover, targeted strategies based on different user segments can aid in more precise product optimization and market promotion. This research provides practical guidance for the further development of AI-generated video tools. Future studies could expand the sample size and data dimensions to further validate and deepen these conclusions.
Keywords
AI-generated video tools, ridge regression, K-means clustering, LDA topic modeling.
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Cite this article
Hu,Y. (2024). User Behavior and Satisfaction in AI-Generated Video Tools: Insights from Surveys and Online Comments. Applied and Computational Engineering,94,136-145.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
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