A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

作者：

Highlights：

• A novel iterated greedy based feature selection algorithm for sentiment analysis.

• A greedy selection procedure that benefits from pre-calculated filter-based scores.

• Outperforms state-of-the-art results for 9 public sentiment classification datasets used.

摘要

•A novel iterated greedy based feature selection algorithm for sentiment analysis.•A greedy selection procedure that benefits from pre-calculated filter-based scores.•Outperforms state-of-the-art results for 9 public sentiment classification datasets used.

论文关键词：Sentiment classification,Feature selection,Iterated greedy,Metaheuristic,Machine learning

论文评审过程：Received 30 August 2019, Revised 5 December 2019, Accepted 2 January 2020, Available online 3 January 2020, Version of Record 9 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113176