Published Paper Details:

With the era of personalized entertainment, it is essential to get movie recommendations spot on with the user sentiments. Our paper introduces SentimentSync an artificial intelligence-based kannada movie recommendation system that relies on sentimental analysis of YouTube trailer comments and web scraping of dynamic movie listings of the Times of India in contrast to other traditional recommendation systems SentimentSync combines locally hosted sentimental analysis using NLTK's Vader with sophisticated web-scraping techniques through selenium the system generates aggregate sentiment scores for a user-queried film and a group of upcoming releases then rank-filter and returns recommendations based on similarity. An interactive flask web interface shows recommendations with an auto-generated explanation utilizing large language models (llms) through the langchain platform. Experimental outcome shows that our hybrid solution increases recommendation relevance as well as the ease of using an interactive web interface over having to use a paid APIs.