Movie Recommendation Engine with Sentiment Analysis: A Survey

Authors: Sonali Rathore, Seema Narvare, Satish Chadokar

Country: India

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Abstract: As Artificial Intelligence and Machine Learning have grown at a rapid pace in recent years, so has the amount of data on the internet. As a result, consumers find it difficult to select the precise information they desire, and learners find it difficult to suggest users exactly what they require. Here, recommendation systems come into play to point consumers in the direction of the content based on their preferences. This study aims to explain the creation and implementation of Movie Recommendation Systems in the Context of Recommendation of Movies and TV Shows on Online Streaming Platforms.

Movie proposal in Web climate is fundamentally significant for Internet clients. It completes thorough accumulation of client's inclinations, surveys, and feelings to help them find appropriate motion pictures advantageously. Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Recommendation System is a smart system that offers pertinent information regarding the decisions a user has made. Collaborative filtering and content-based filtering are two of its useful techniques. this paper is aimed to explain making and implementation of Movie Recommendation Systems Using Machine Learning Algorithms, Sentiment Analysis and Cosine Similarity.

Keywords: Recommendation System, Content-based Filtering, Collaborating Filtering, Sentiment Analysis, Cosine Similarity

Paper Id: 230224

Published On: 2023-06-16

Published In: Volume 11, Issue 3, May-June 2023

Cite This: Movie Recommendation Engine with Sentiment Analysis: A Survey - Sonali Rathore, Seema Narvare, Satish Chadokar - IJIRMPS Volume 11, Issue 3, May-June 2023.

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