Hybrid Movie Recommendation with Sentimental Analysis

Abstract

Recommendation systems are common in recent years. It is a software tool and techniques that provide suggestions based on the customer's taste to discover new appropriate things for them by filtering personalised information based on the user's preferences from a large volume of information. It is a software tool to filter personalised information for each user of the system from a large dataset. Movie recommendation systems are common in recent years. Various algorithms and techniques used in recommendation systems are compared in this research. Then requirements were gathered from stakeholders and the system was explained by various diagrams. Most of the stakeholders preferred to improve the performance and accuracy of movie recommendation systems based on surveys. In this research a movie recommendation system is built using K-Means clustering, KNN and Sentiment analysis technique. The system is developed using python programming language. Steps involved in the implementation process are explained and testing evaluation of the system is further discussed. The evaluators were satisfied with the accuracy and performance of the movie recommendation system. The research uncovered many new research questions and areas for the research community to develop the solution further.