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Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning

Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning

Razia Sulthana A., Jaithunbi A. K., Haritha Harikrishnan, Vijayakumar Varadarajan
Copyright: © 2022 |Volume: 17 |Issue: 1 |Pages: 23
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799894001|DOI: 10.4018/IJITWE.311428
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MLA

Razia Sulthana A., et al. "Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning." IJITWE vol.17, no.1 2022: pp.1-23. http://doi.org/10.4018/IJITWE.311428

APA

Razia Sulthana A., Jaithunbi A. K., Harikrishnan, H., & Varadarajan, V. (2022). Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning. International Journal of Information Technology and Web Engineering (IJITWE), 17(1), 1-23. http://doi.org/10.4018/IJITWE.311428

Chicago

Razia Sulthana A., et al. "Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning," International Journal of Information Technology and Web Engineering (IJITWE) 17, no.1: 1-23. http://doi.org/10.4018/IJITWE.311428

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Abstract

The internet makes it easier for people to connect to each other and has become a platform to express ideas and share information with the world. The growth of the internet has indirectly led to the development of social networking sites. The reviews posted by people on these sites implies their opinion, and analysis over reviews is required to understand their intent. In this paper, natural language processing technique and machine learning algorithms are applied to classify the text data. The contributions of the proposed approach are three-fold: 1) chi square selector is applied to select the k-best features, 2) support vector machines is executed to classify the reviews (hyperparameters of the SVM classifier are tuned using GridSearch approach), and 3) bagging algorithm is applied with the base classifier over the newly built SVM classifier. The number of base classifiers of the bagging algorithm is varied accordingly. The results of the proposed approach are compared to the similar existing work, and hence, it is found to achieve better results as compared to the existing systems.