Text Mining and Latent Topic Discovery in TED Talks
M Patel · International Journal for Research in Applied Science and Engineering Technology · 2025
The TED Talks contain a vast amount of knowledge in different fields; this implies that making it useful to learn and share ideas gives an edge. With thousands of the talks at hand, it is challenging to organize them properly and suggest meaningful content. The technique of text mining and latent topic discovery draws on methods applied to the discovery of latent thematic structures from the transcripts of TED Talks by way of NLP through techniques such as LDA and NMF. The developed method could automatically identify top terms, group related talks together, and organize the content in such a way to better enhance the recommendation systems of speaker discourse. This work supports content discovery personalization and helps in solving the problems identified above under biased content propagation. The outcome enhances structured exploration for TED Talks and reaches better user engagement and facilitates more efficient knowledge dissemination.