Gokalp, OsmanTasci, ErdalUgur, Aybars2020-12-012020-12-0120200957-41741873-67930957-41741873-6793https://doi.org/10.1016/j.eswa.2020.113176https://hdl.handle.net/11454/62380In 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.en10.1016/j.eswa.2020.113176info:eu-repo/semantics/closedAccessSentiment classificationFeature selectionIterated greedyMetaheuristicMachine learningArticle146WOS:0005196534000172-s2.0-85077510592N/AQ1