{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:02:19Z","timestamp":1781103739323,"version":"3.54.1"},"reference-count":26,"publisher":"IGI Global Scientific Publishing","issue":"1","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"},{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/deed.en_US"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,17]]},"abstract":"<p>This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Na\u00efve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer\u2019s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.<\/p>","DOI":"10.4018\/ijamc.2022010107","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T08:51:09Z","timestamp":1635497469000},"page":"1-21","source":"Crossref","is-referenced-by-count":15,"title":["Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)"],"prefix":"10.4018","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3201-4285","authenticated-orcid":true,"given":"Parag","family":"Verma","sequence":"first","affiliation":[{"name":"Uttaranchal University, Dehradun, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankur","family":"Dumka","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Women Institute of Technology, Dehradun, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anuj","family":"Bhardwaj","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Chandigarh University, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alaknanda","family":"Ashok","sequence":"additional","affiliation":[{"name":"College of Technology, G. B. Pant University of Agriculture and Technology, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJAMC.2022010107-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.04.071"},{"key":"IJAMC.2022010107-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.07.007"},{"key":"IJAMC.2022010107-2","first-page":"440","article-title":"Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification.","author":"J.Blitzer","year":"2007","journal-title":"Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics"},{"key":"IJAMC.2022010107-3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2016.09.005"},{"key":"IJAMC.2022010107-4","unstructured":"Chang, C.-C. (2001). A library for support vector machines.Http:\/\/Www. Csie. Ntu. Edu. 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