Jun Ikram
Adaptive ensemble learning models have gained significant attention in sentiment analysis due to their ability to improve classification accuracy and adaptability to varying datasets. Sentiment analysis, a subfield of Natural Language Processing (NLP), involves determining the sentiment polarity of a given text, whether positive, negative, or neutral. Traditional machine learning models often struggle with sentiment classification due to data complexity, subjectivity, and evolving linguistic patterns. To address these challenges, an adaptive ensemble learning model utilizing evolutionary computing is proposed, integrating multiple classifiers and optimizing their combination dynamically. Ensemble learning techniques involve combining multiple weak classifiers to create a more robust predictive model. Popular ensemble methods include bagging, boosting, and stacking. Each of these approaches has advantages, but they may not always adapt efficiently to diverse datasets. Evolutionary computing, inspired by natural selection, provides a solution by dynamically optimizing the ensemble structure and classifier weights based on dataset characteristics. The combination of these two methodologies results in a powerful adaptive sentiment analysis model.
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