{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:39:39Z","timestamp":1760240379981,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T00:00:00Z","timestamp":1558569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Na\u00efve Bayes (NB). For the classification of sentiments, 15\u201320% improvement has been recorded. The dataset used for classification of sentiments is Blitzer\u2019s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.<\/jats:p>","DOI":"10.3390\/data4020076","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T02:22:00Z","timestamp":1558664520000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm"],"prefix":"10.3390","volume":"4","author":[{"given":"Mehreen","family":"Naz","sequence":"first","affiliation":[{"name":"Department of Computer Science; National University of Computer and Emerging Sciences, Lahore 54770, Pakistan"}]},{"given":"Kashif","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Computer Science; National University of Computer and Emerging Sciences, Lahore 54770, Pakistan"}]},{"given":"Ayesha","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Science and Technology; University of Management and Technology, Lahore 54782, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,23]]},"reference":[{"key":"ref_1","first-page":"36","article-title":"Using Bio-inspired intelligence for Web opinion Mining","volume":"87","author":"Stylios","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6676","DOI":"10.1016\/j.eswa.2014.05.009","article-title":"Forest optimization algorithm","volume":"41","author":"Ghaemi","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_3","unstructured":"Feizi-Derakhshi, M.R., and Ghaemi, M. (2014, January 8\u20139). Classifying different feature selection algorithms based on the search strategies. Proceedings of the International Conference on Machine Learning, Electrical and Mechanical Engineering, Dubai, United Arab Emirates."},{"key":"ref_4","unstructured":"(2017, January 02). Feature Selection. Available online: https:\/\/en.wikipedia.org\/wiki\/Feature_selection."},{"key":"ref_5","unstructured":"Halim, Z., Atif, M., Rashid, A., and Edwin, C.A. (2017). Profiling players using real-world datasets: Clustering the data and correlating the results with the big-five personality traits. IEEE Trans. Affect. Comput."},{"key":"ref_6","unstructured":"Mensikova, A., and Mattmann, C.A. (2017, January 15). Ensemble Sentiment Analysis to Identify Human Trafficking in Web Data. Available online: http:\/\/www.hrl.com\/laboratories\/issl\/ccni\/workshop\/gta3\/papers\/GTA3_paper_5.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.ijar.2017.07.013","article-title":"Efficient clustering of large uncertain graphs using neighborhood information","volume":"90","author":"Halim","year":"2017","journal-title":"Int. J. Approx. Reason."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.ins.2016.12.035","article-title":"Quantifying and optimizing visualization: An evolutionary computing-based approach","volume":"385","author":"Halim","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s13042-015-0347-4","article-title":"Sentimental feature selection for sentiment analysis of Chinese online reviews","volume":"9","author":"Zheng","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.asoc.2016.08.039","article-title":"Employing artificial neural networks for constructing metadata-based model to automatically select an appropriate data visualization technique","volume":"49","author":"Muhammad","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, Z., Hu, J., Ding, W., and Zheng, X. (2015, January 23\u201325). Review sentiment analysis based on deep learning. Proceedings of the 2015 IEEE 12th International Conference on e-Business Engineering, Beijing, China.","DOI":"10.1109\/ICEBE.2015.24"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/MIS.2016.31","article-title":"Affective computing and sentiment analysis","volume":"31","author":"Cambria","year":"2016","journal-title":"IEEE Intell. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1007\/s13042-018-0793-x","article-title":"An efficient automatic multiple objectives optimization feature selection strategy for internet text classification","volume":"10","author":"Huang","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","first-page":"12","article-title":"Feature reduction based on genetic algorithm and hybrid model for opinion mining","volume":"2015","author":"Kalaivani","year":"2015","journal-title":"Sci. Program."},{"key":"ref_16","unstructured":"Pak, A., and Paroubek, P. (2010, January 19\u201321). Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC), Valletta, Malta."},{"key":"ref_17","first-page":"139","article-title":"Sentiment analysis of movie reviews using hybrid method of naive bayes and genetic algorithm","volume":"3","author":"Govindarajan","year":"2013","journal-title":"Int. J. Adv. Comput. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/1361684.1361685","article-title":"Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums","volume":"26","author":"Abbasi","year":"2008","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.proeng.2012.06.005","article-title":"A novel feature selection algorithm using particle swarm optimization for cancer microarray data","volume":"38","author":"Sahu","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chachra, A., Mehndiratta, P., and Gupta, M. (2017, January 10\u201312). Sentiment analysis of text using deep convolution neural networks. Proceedings of the 2017 Tenth International Conference on Contemporary Computing (IC3), NOIDA, India.","DOI":"10.1109\/IC3.2017.8284327"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.proeng.2013.02.059","article-title":"Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization","volume":"53","author":"Basari","year":"2013","journal-title":"Procedia Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Seal, A., Ganguly, S., Bhattacharjee, D., Nasipuri, M., and Gonzalo-Martin, C. (2015). Feature Selection using Particle Swarm Optimization for Thermal Face Recognition. Applied Computation and Security Systems, Springer.","DOI":"10.1007\/978-81-322-1985-9_2"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3821","DOI":"10.1007\/s00500-016-2093-2","article-title":"Particle swarm optimization-based feature selection in sentiment classification","volume":"20","author":"Shang","year":"2016","journal-title":"Soft Comput."},{"key":"ref_24","first-page":"1667","article-title":"Feature subset selection based on bio-inspired algorithms","volume":"27","author":"Yun","year":"2011","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_25","first-page":"35","article-title":"Multi-Classifier Systems: Review and a roadmap for developers","volume":"3","author":"Ranawana","year":"2006","journal-title":"Int. J. Hybrid Intell. Syst."},{"key":"ref_26","unstructured":"Blitzer, J., Dredze, M., and Pereira, F. (2007, January 23\u201330). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic."},{"key":"ref_27","unstructured":"Porter, M. (2017, March 20). The Porter Stemming Algorithm. Available online: http:\/\/tartarus.org\/martin\/PorterStemmer\/."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/2\/76\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:54:39Z","timestamp":1760187279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/4\/2\/76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,23]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["data4020076"],"URL":"https:\/\/doi.org\/10.3390\/data4020076","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2019,5,23]]}}}