Music and Movie Recommendation System

Music and Movie Recommendation System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2018 by IJETT Journal
Volume-61 Number-3
Year of Publication : 2018
Authors : Puja Deshmukh, Geetanjali Kale
DOI :  10.14445/22315381/IJETT-V61P229

Citation 

MLA Style: Puja Deshmukh, Geetanjali Kale "Music and Movie Recommendation System" International Journal of Engineering Trends and Technology 61.3 (2018): 178-181.

APA Style:Puja Deshmukh, Geetanjali Kale (2018). Music and Movie Recommendation System. International Journal of Engineering Trends and Technology, 61(3), 178-181.

Abstract
The advancement in technology has led to massive use of internet. People use social networking sites almost every day. These sites help people to express themselves to the online society. People use various posts to express themselves, these posts are nothing but short informal texts having positive, negative or neutral emotions. Music is an important aspect of human life. People prefer to listen to music more often than any other activity. With the internet technology, a huge amount of music content containing music of various genres has become easily available to millions of users around the world. Music collection since decades and comprising of various genres of music is available. The major difficulty that the users face is to select appropriate music from such huge collection. Similarly, a huge collection of movies comprising of various genres is also available. Music and movie recommender will recommend music and movies to the user based on their mood along with an emoticon symbolizing their mood. The mood of the user will be derived by performing sentiment analysis of the user posts. In order to provide better recommendations, sentiment analysis will be performed on the lyrics of the songs using NLP. Random forest algorithm will be used for classifying the lyrics into various categories (happy, sad, joyful).

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Keywords
Data Mining, Recommendation System, Social Network, Sentiment Analysis, Movie Recommendation.