Improving Opinion Mining Accuracy with Dragonfly Algorithm-Based Hybrid Classification
Keywords:
Machine Learning, Dragonfly, Hybrid, TF-IDF, Datasets.Abstract
The paper proposes the Dragonfly + Hybrid Classifier, a novel approach designed to enhance opinion mining across diverse datasets. Leveraging the Dragonfly algorithm for feature set selection and combining it with a hybrid classification method, this innovative approach offers the potential for more accurate and reliable predictions. On the Twitter Sentiment dataset, notorious for its dynamic and noisy nature, the Dragonfly + Hybrid Classifier excels with an average precision of approximately 0.93498, recall of approximately 0.92965, and an F-measure of approximately 0.93208, alongside an average accuracy of around 96.134%. Within the Movie Review dataset, where opinions are nuanced and context-dependent, the Dragonfly + Hybrid Classifier secures an impressive average precision of approximately 0.91348, coupled with an average recall of approximately 0.9189, achieving an F-measure of approximately 0.91582 and maintaining an average accuracy of around 94.98%. In the context of the Depression dataset, where sensitivity and accuracy are paramount, the Dragonfly + Hybrid Classifier excels with an average precision of approximately 0.9627, an average recall of approximately 0.966, an F-measure of approximately 0.9643, and an average accuracy of around 94.62%. These findings collectively affirm the Dragonfly + Hybrid Classifier as a potent tool for opinion analysis across diverse domains, positioning it as a valuable asset in field of opinion mining and analysis applications, particularly in domains where opinion understanding is paramount.
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