| TITLE |
Review & Rating Based Film Sentiment Classification using Machine Learning Technique |
| ABSTRACT |
In today's world, there is a possibility that the success of a film has increased more and more about numbers. There are many ways from which these films are classified as a hit or flop using the audience review and rating score through ML methods. Rather than relying on the box office alone as a cure, this function assigns weight to the influence of public opinion in determining how well a movie actually performs. We applied standard NLP methods including word tokens, stop-ward elimination, Term Frequency – Inverse Document Frequency to translate text reviews into numeric features. The ratings with text features to enhance the prediction model were also generalized and combined. Two classification algorithm logistic regression and support vector machine were utilized for classification. The accuracy of the SVM model is attained at 96.36% while logistic regression was 98.18% - impressive performance
in predicting the categories of the film's success. This mixture of a combination of reviews and ratings is an intelligent,
scalable method for predicting the success of the film with the opinion of the real audience through the user input and
the user input. |
| AUTHOR |
Abdul Nasir, Hemanth Kumar |
| PUBLICATION DATE |
2025-09-03 19:02:26 |
| VOLUME |
13 |
| ISSUE |
3 |
| DOI |
DOI: 10.15662/IJMSERH.2025.1303070 |
| PDF |
pdf/2025/7/70_Review & Rating Based Film Sentiment Classification using Machine Learning Technique.pdf |
| KEYWORDS |
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