With over 4.5 billion internet users worldwide, online video engagement and entertainment remain the most popular activities among users. In this context, social media platforms such as YouTube have emerged as a growing source of data, the efficient analysis of which has become even more critical. This thesis, based on the practical experience at Jato, exploits the potential of this sheer volume of web-sourced data, to perform sentiment and competitive analysis of automotive related content and draw market intelligence. The work leverages a framework of Large Language Models (LLMs) to process large amounts of textual data obtained from YouTube comments and video transcripts pertaining to vehicles in the British C-segment market.