Sentiment Analysis on Netflix

• Author(s): Priti Bagkar ; Aishwarya Borude ; Zarrin Aga
• Paper ID: 1702717
• Page: 121-126
• Published Date: 21-05-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 4 Issue 11 May-2021

Abstract

We use machine learning to build a personalized movie scoring and recommendation system based on the user's previous movie ratings. Different people have different taste in movies, and this is not reflected in a single score that we see when we Google a movie. Our movie scoring system helps users instantly discover movies to their liking, regardless of how distinct their tastes may be. Current recommender systems generally fall into two categories: content-based filtering and collaborative filtering. We experiment with both approaches in our project. For content-based filtering, we take movie features such as review and keywords as inputs and use TF-IDF and doc2vec to calculate the similarity between movies. For collaborative filtering, the input to our algorithm is the observed users’ movie rating, and we use K-nearest neighbors and matrix factorization to predict user’s movie ratings. We found that collaborative filtering performs better than content-based filtering in terms of prediction error and computation time.

Keywords

Sentiments, NLU, LSTM, Neural Network, Recommendation

Citations

IRE Journals:
Priti Bagkar , Aishwarya Borude , Zarrin Aga "Sentiment Analysis on Netflix" Iconic Research And Engineering Journals Volume 4 Issue 11 2021 Page 121-126

IEEE:
Priti Bagkar , Aishwarya Borude , Zarrin Aga "Sentiment Analysis on Netflix" Iconic Research And Engineering Journals, 4(11)