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A Hybridized GA-Based Feature Selection for Text Sentiment Analysis
Abstract
Recent research work has described the effectiveness of various sentiment classification techniques ranging from simple lexicon-based methods to more complex machine learning techniques. Researchers of the article develop an integrated framework that bridges the gap between dictionary-based methods and machine learning methods to achieve better accuracy and more flexibility. To solve the problem of scalability that occurs as the feature set grows, a hybrid genetic algorithm (GA)-based dimensional reduction method is proposed. With the help of this novel approach, authors can reduce the size of the feature set by reaching a remarkable value of accuracy. Here the authors have compared the proposed feature reduction method with a widely used principal component analysis and singular value decomposition-based feature reduction algorithms. In addition, the proposed sentiment analysis model is tested in other metrics, including precision, recall, F1 score, and feature size.
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