Paper Title
IN-DEPTH VIDEO ANALYSIS AND CONTENT SUMMARIZATION

Abstract
In the realm of multimedia content, the proliferation of video data presents a significant challenge in efficiently accessing and digesting relevant information. Video summarization, particularly through the extraction of captions using the YouTube Transcript API, offers a promising solution to this challenge. This paper proposes a video summarization framework that leverages the YouTube Transcript API to extract textual captions from videos, enabling the automatic generation of concise summaries. Additionally, the framework incorporates sentiment analysis of the extracted captions using NLTK’s VADER lexicon method to discern the emotional tone conveyed in the video content. By utilizing natural language processing techniques to analyze and condense the extracted captions, alongside sentiment analysis, the proposed framework aims to distill key insights and highlights while capturing the emotional context of the video content. This research enhances the progression of techniques for summarizing videos, facilitating improved information retrieval, comprehension, and emotional understanding in multimedia applications. Keywords - Natural Language Processing, Vader lexicon, Natural Language Toolkit