Augmenting Sentiment Analysis Prediction in Binary Text Classification through Advanced Natural Language Processing Models and Classifiers
Автор: Zhengbing Hu, Ivan Dychka, Kateryna Potapova, Vasyl Meliukh
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 2 Vol. 16, 2024 года.
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Sentiment analysis is a critical component in natural language processing applications, particularly for text classification. By employing state-of-the-art techniques such as ensemble methods, transfer learning and deep learning architectures, our methodology significantly enhances the robustness and precision of sentiment predictions. We systematically investigate the impact of various NLP models, including recurrent neural networks and transformer-based architectures, on sentiment classification tasks. Furthermore, we introduce a novel ensemble method that combines the strengths of multiple classifiers to improve the predictive ability of the system. The results demonstrate the potential of integrating state-of-the-art Natural Language Processing (NLP) models with ensemble classifiers to advance sentiment analysis. This lays the foundation for a more advanced comprehension of textual sentiments in diverse applications.
Binary Text Classification, Natural Language Processing, Deep Learning, Ensemble Methods, Neural Networks
Короткий адрес: https://sciup.org/15019383
IDR: 15019383 | DOI: 10.5815/ijitcs.2024.02.02
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