Abstract
Sentiment analysis or opinion mining as a research field is gaining importance. In this study, we are focusing on building a sentiment analyzer for Amazon product reviews in multi-domain category using ensemble machine learning algorithms. We have used five machine learning algorithms random forest, extra trees, bagging, AdaBoost, and stochastic gradient boosting for our experiments with four datasets, books, DVD, kitchenware, and electronics categories. We have compared the models to explore which model gives better performance in analyzing sentiments. The result shows that stochastic gradient boosting with 83% accuracy outperforms the other algorithms including the random forest algorithm which is generally considered as the best.
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Dhamayanthi, N., Lavanya, B. (2021). Sentiment Analysis Framework for E-Commerce Reviews Using Ensemble Machine Learning Algorithms. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_34
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DOI: https://doi.org/10.1007/978-981-16-0171-2_34
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