Enhancement of Machine Learning Algorithm in Fine-grained Sentiment Analysis Using the Ensemble
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Abstract
Fine-grained sentiment analysis plays a crucial role in extracting subtle opinions from textual data, especially in domains such as customer reviews and social media analysis. Traditional machine learning models, including Support Vector Machines (SVM), Naïve Bayes, and Decision Tree, often face limitations in accurately classifying fine-grained sentiments due to their inability to generalize well in complex classication tasks. To address this challenge, this study proposes an ensemble learning approach integrating voting, bagging, boosting, and stacking to enhance sentiment classification performance. Experiments were conducted on multiple datasets, comparing standalone classiers and ensemble-based approaches. The results indicate that stacking-based ensemble models achieve the highest accuracy, reaching 92.45%, outperforming traditional classiers such as SVM (88.23%) and Naïve Bayes (85.67%). Additionally, ensemble methods demonstrate improved generalization and robustness, reducing misclassification rates by 6% on average compared to individual classifiers. Among the tested ensemble techniques, stacking consistently delivered superior results, leveraging diverse weak learners to optimize sentiment classication accuracy. This research highlights the eectiveness of ensemble learning in fine-grained sentiment analysis, oering a robust methodology for improving classication accuracy and reducing sentiment misclassication. The ndings suggest that ensemble approaches, particularly stacking, provide a more reliable and scalable solution for sentiment analysis tasks, making them suitable for real-world applications in natural language processing.
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