The rapid rise of digital music and movie platforms has resulted in an overabundance of options for users, making it increasingly difficult for people to choose content that matches their emotional inclinations. In response, this paper provides a Python-based mood analyser. The suggested system analyses textual data connected with music and movie information, user reviews, and emotions expressed in multiple sources using natural language processing techniques and machine learning algorithms. The technology can recognise the emotional features present in the content and then offer personalised music tracks and films to users based on their desired emotional state by using sentiment analysis and emotion identification algorithms. The algorithm intends to refine its recommendations and increase the accuracy of its emotional analysis by harnessing the large quantity of data accessible. The system may change and evolve as a result of this continual learning process, ensuring that users receive the most relevant and personalised material based on their emotional preferences