Text to Video Generation using Natural Language Processing and Machine Learning

Press Reports in general have been published in text based format for a vast period of time, although the old way of publishing reports may have been effective initially, it has led to a decrease in audience engagement and attention span. The study offers a novel solution to the problem of users’ attention spans getting shorter. Therefore, to retain user engagement and increase content quality, the proposed solution aims to create an automated system that uses press reports to generate dynamic video content. This technology improves user comprehension and engagement by utilizing audio-visual synthesis and natural language processing techniques. The models that are implemented have performed well during the Testing Phase by showing an average accuracy of 82 percentage from the NLP Large Language Models and 89 percentage from the Machine Learning Models and the outcomes reveal increased knowledge retention and user engagement. Finally, this study offers a novel method of communication by automatically converting press releases into videos for maximum viewer engagement.