International Journal For Multidisciplinary Research

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Content Recommendation System using Sentiment Analysis and Spoiler Detection

Author(s) Prathamesh Renghe, Vaibhav Udamale, Tejas Vaidya, Aparna Halbe
Country India
Abstract This web application is a user-friendly platform for movie and TV show enthusiasts. It provides detailed information about each title, including plot summaries, genres, durations, ratings, and the cast. Users can watch trailers to get a preview of the content. Leveraging a massive movie database, the app curates custom suggestions tailored to each user's unique taste, utilizing cosine similarity. The app takes recommendations a step further by utilizing a content-based system that analyzes user reviews. The sentiment analysis helps identify positive and negative feedback. To preserve the viewing experience, the system includes a spoiler detection mechanism that hides potential spoilers from user reviews. Users can create their own watchlists to keep track of desired content. With machine learning algorithms, the application continuously improves its recommendations, ensuring users find engaging content tailored to their tastes. This web app delivers a seamless and user-centric approach to discovering and enjoying movies and TV shows, emphasizing the joy of surprise while exploring reviews and suggestions.
Keywords Movie Recommender System, Content Based Filtering, Sentiment Analysis, Spoiler Detection
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-30
Cite This Content Recommendation System using Sentiment Analysis and Spoiler Detection - Prathamesh Renghe, Vaibhav Udamale, Tejas Vaidya, Aparna Halbe - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.19127
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.19127
Short DOI https://doi.org/gts4qm
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