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A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification
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DOI：10.1016/j.eswa.2020.113176.png)
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Abstract
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In recent years, sentiment analysis is becoming more and more important as the number of digital text resources increases in parallel with the development of information technology. Feature selection is a crucial sub-stage for the sentiment analysis as it can improve the overall predictive performance of a classifier while reducing the dimensionality of a problem. In this study, we propose a novel wrapper feature selection algorithm based on Iterated Greedy (IG) metaheuristic for sentiment classification. We also develop a selection procedure that is based on pre-calculated filter scores for the greedy construction part of the IG algorithm. A comprehensive experimental study is conducted on commonly-used sentiment analysis datasets to assess the performance of the proposed method. The computational results show that the proposed algorithm achieves 96.45% and 90.74% accuracy rates on average by using Multi-nomial Naive Bayes classifier for 9 public sentiment and 4 Amazon product reviews datasets, respectively. The results also reveal that our algorithm outperforms state-of-the-art results for the 9 public sentiment datasets. Moreover, the proposed algorithm produces highly competitive results with state-of-the-art feature selection algorithms for 4 Amazon datasets. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Sentiment classification
Feature selection
Iterated greedy
Metaheuristic
Machine learning
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