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Target Sentiment Analysis Ensemble for Product Review Classification

Rhoda Viviane Achieng Ogutu, Richard M. Rimiru, Calvins Otieno

Source Title: Journal of Information Technology Research (JITR)15(1)

ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.299382

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MLA

Ogutu, Rhoda Viviane Achieng, et al. "Target Sentiment Analysis Ensemble for Product Review Classification." JITR vol.15, no.1 2022: pp.1-13. http://doi.org/10.4018/JITR.299382

APA

Ogutu, R. V., Rimiru, R. M., & Otieno, C. (2022). Target Sentiment Analysis Ensemble for Product Review Classification. Journal of Information Technology Research (JITR), 15(1), 1-13. http://doi.org/10.4018/JITR.299382

Chicago

Ogutu, Rhoda Viviane Achieng, Richard M. Rimiru, and Calvins Otieno. "Target Sentiment Analysis Ensemble for Product Review Classification," Journal of Information Technology Research (JITR) 15, no.1: 1-13. http://doi.org/10.4018/JITR.299382

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Abstract

Abstract— Machine learning can be used to provide systems the ability to automatically learn and improve from experiences without being explicitly programmed. It is fundamentally a multidisciplinary field that draws on results from Artificial intelligence, probability and statistics, information theory and analysis, among other fields that impact the field of Machine Learning. Ensemble methods are techniques that can be used to improve the predictive ability of a Machine Learning model. An ensemble comprises of individually trained classifiers whose predictions are combined when classifying instances. Some of the currently popular ensemble methods include Boosting, Bagging and Stacking. In this paper, we review these methods and demonstrate why ensembles can often perform better than single models. Additionally, some new experiments are presented to demonstrate the computational ability of Stacking approach.