{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:39:29Z","timestamp":1769636369382,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Doctoral Research Grant, Ministry of Research, Technology and Higher Education Indonesia","award":["3790\/IT3.L1\/PT.01.03\/P\/B\/2022"],"award-info":[{"award-number":["3790\/IT3.L1\/PT.01.03\/P\/B\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>(1) Background: Feature selection is the biggest challenge in feature-rich sentiment analysis to select the best (relevant) feature set, offer information about the relationships between features (informative), and be noise-free from high-dimensional datasets to improve classifier performance. This study aims to propose a binary version of a metaheuristic optimization algorithm based on Swarm Intelligence, namely the Salp Swarm Algorithm (SSA), as feature selection in sentiment analysis. (2) Methods: Significant feature subsets were selected using the SSA. Transfer functions with various types of the form S-TF, V-TF, X-TF, U-TF, Z-TF, and the new type V-TF with a simpler mathematical formula are used as a binary version approach to enable search agents to move in the search space. The stages of the study include data pre-processing, feature selection using SSA-TF and other conventional feature selection methods, modelling using K-Nearest Neighbor (KNN), Support Vector Machine, and Na\u00efve Bayes, and model evaluation. (3) Results: The results showed an increase of 31.55% to the best accuracy of 80.95% for the KNN model using SSA-based New V-TF. (4) Conclusions: We have found that SSA-New V3-TF is a feature selection method with the highest accuracy and less runtime compared to other algorithms in sentiment analysis.<\/jats:p>","DOI":"10.3390\/computation11030056","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T02:33:16Z","timestamp":1678329196000},"page":"56","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Feature Selection Using New Version of V-Shaped Transfer Function for Salp Swarm Algorithm in Sentiment Analysis"],"prefix":"10.3390","volume":"11","author":[{"given":"Dinar Ajeng","family":"Kristiyanti","sequence":"first","affiliation":[{"name":"Departement of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor 16680, Indonesia"},{"name":"Departement of Information System, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Tangerang 15810, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2280-1752","authenticated-orcid":false,"given":"Imas Sukaesih","family":"Sitanggang","sequence":"additional","affiliation":[{"name":"Departement of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor 16680, Indonesia"}]},{"given":"Annisa","family":"Annisa","sequence":"additional","affiliation":[{"name":"Departement of Computer Science, Faculty of Mathematics and Natural Sciences, IPB University, Bogor 16680, Indonesia"}]},{"given":"Sri","family":"Nurdiati","sequence":"additional","affiliation":[{"name":"Departement of Mathematics, Faculty of Mathematics and Natural Sciences, IPB University, Bogor 16680, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","unstructured":"(2020, February 02). 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