Machine Learning Techniques to Classify Movies Based on Reviews

Vishva Dalela; Kunal Gupta; G. Poornalatha

doi:10.30919/es1391

Machine Learning Techniques to Classify Movies Based on Reviews

Vishva Dalela
, Kunal Gupta
, G. Poornalatha^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Movie reviews help viewers decide whether to watch the movie based on detailed reviews with analysis by other viewers and experts. It is an important indicator for the audience as a guiding choice amidst vast entertainment options. This paper aims to analyze movie reviews and examine the accuracies of different machine learning algorithms. The review data is pre-processed to transform into a format suitable to the model. We feed this pre-processed data to various models for the best possible outcome. It was observed that the support vector machine yields good results for the datasets considered. Furthermore, k-fold cross-validation is carried out to compare and check the efficiency of various models.

Original language	English
Article number	1391
Journal	Engineered Science
Volume	33
DOIs	https://doi.org/10.30919/es1391
Publication status	Published - 02-2025

All Science Journal Classification (ASJC) codes

Chemistry (miscellaneous)
General Materials Science
Energy Engineering and Power Technology
General Engineering
Physical and Theoretical Chemistry
Artificial Intelligence
Applied Mathematics

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title = "Machine Learning Techniques to Classify Movies Based on Reviews",

abstract = "Movie reviews help viewers decide whether to watch the movie based on detailed reviews with analysis by other viewers and experts. It is an important indicator for the audience as a guiding choice amidst vast entertainment options. This paper aims to analyze movie reviews and examine the accuracies of different machine learning algorithms. The review data is pre-processed to transform into a format suitable to the model. We feed this pre-processed data to various models for the best possible outcome. It was observed that the support vector machine yields good results for the datasets considered. Furthermore, k-fold cross-validation is carried out to compare and check the efficiency of various models.",

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Machine Learning Techniques to Classify Movies Based on Reviews

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