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, vol. 4, no. 11, May. 2021

APA:
Priti Bagkar, Aishwarya Borude, Zarrin Aga (2021). Sentiment Analysis on Netflix. Iconic Research And Engineering Journals, 4(11).

MLA:
Priti Bagkar, Aishwarya Borude, Zarrin Aga "Sentiment Analysis on Netflix" Iconic Research And Engineering Journals, vol. 4, no. 11, May. 2021.

BibTeX

@article{1702717,
author = {Priti Bagkar, Aishwarya Borude, Zarrin Aga},
title = {Sentiment Analysis on Netflix},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {11},
pages = {121-126},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702717.pdf},
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},
month = {May}
}