Manuscript Number : SHISRRJ247255

Movies Recommendation System Using Hybrid Filtering and Naive Bayes Algorithm

Authors(2) :-B. Chandini, B. Rupadevi

There is a growing need to develop a strong sentiment analysis model that categorizes movie evaluations because customer opinions can improve product efficiency and because reviews determine whether a movie succeeds or fails. Tokenization is used in this paper to convert the input string into a vector of words; stemming is used to extract word roots; feature selection is used to extract key words; and classification is applied to categorize reviews as positive or negative. We construct this model just by utilizing content based collaborative filtering and for the sentiment analysis we are going to use the Naive Bayes algorithm, Collaborative Filtering Algorithms, Content-Based Filtering Algorithms, Hybrid Filtering Algorithms to predict.

Authors and Affiliations

B. Chandini
Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
B. Rupadevi
Associate Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India

Machine Learning, Naive Bayes, Natural Language Processing (NLP), Sentiment Analysis

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Publication Details

Published in : Volume 7 | Issue 2 | March-April 2024
Date of Publication : 2024-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 90-98
Manuscript Number : SHISRRJ247255
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

B. Chandini, B. Rupadevi, "Movies Recommendation System Using Hybrid Filtering and Naive Bayes Algorithm", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 2, pp.90-98, March-April.2024
URL : https://shisrrj.com/SHISRRJ247255

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