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Sentiment Analysis on Netflix
Subject area: Science,Engineering and Technology · Area of research: Information Technology
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
How to cite this paper
@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},
}