FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis

FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis

Автор: Yasin Görmez, Yunus E. Işık, Mustafa Temiz, Zafer Aydın

Журнал: International Journal of Information Technology and Computer Science @ijitcs

Статья в выпуске: 6 Vol. 12, 2020 года.

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Sentiment analysis is the process of determining the attitude or the emotional state of a text automatically. Many algorithms are proposed for this task including ensemble methods, which have the potential to decrease error rates of the individual base learners considerably. In many machine learning tasks and especially in sentiment analysis, extracting informative features is as important as developing sophisticated classifiers. In this study, a stacked ensemble method is proposed for sentiment analysis, which systematically combines six feature extraction methods and three classifiers. The proposed method obtains cross-validation accuracies of 89.6%, 90.7% and 67.2% on large movie, Turkish movie and SemEval-2017 datasets, respectively, outperforming the other classifiers. The accuracy improvements are shown to be statistically significant at the 99% confidence level by performing a Z-test.

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Sentiment analysis, ensemble methods, machine learning, feature extraction

Короткий адрес: https://sciup.org/15017471

IDR: 15017471   |   DOI: 10.5815/ijitcs.2020.06.02

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