Movie Recommendation System Using Sentiment Analysis From Microblogging Data
Recommendation systems (RSs) have garnered immense interest for applications in e-commerce and digital media. Traditional approaches in RSs include such as collaborative filtering (CF) and content-based filtering (CBF) through these approaches that have certain limitations, such as the necessity of prior user history and habits for performing the task of recommendation. To minimize the effect of such limitation, this article proposes a hybrid RS for the movies that leverage the best of concepts used from CF and CBF along with sentiment analysis of tweets from microblogging sites. The purpose to use movie tweets is to understand the current trends, public sentiment, and user response of the movie. Experiments conducted on the public database have yielded promising results.
PROJECT OUTPUT VIDEO:
- System : Pentium i3 Processor
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 2 GB
- Operating system : Windows 10.
- Coding Language : JAVA
- Tool : Netbeans 8.2
- Database : MYSQL
Sudhanshu Kumar , Kanjar De, and Partha Pratim Roy, “Movie Recommendation System Using Sentiment Analysis From Microblogging Data”, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2020.