{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T23:46:28Z","timestamp":1773186388493,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,8]],"date-time":"2018-12-08T00:00:00Z","timestamp":1544227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Sentiment classification (SC) is a reference to the task of sentiment analysis (SA), which is a subfield of natural language processing (NLP) and is used to decide whether textual content implies a positive or negative review. This research focuses on the various machine learning (ML) algorithms which are utilized in the analyzation of sentiments and in the mining of reviews in different datasets. Overall, an SC task consists of two phases. The first phase deals with feature extraction (FE). Three different FE algorithms are applied in this research. The second phase covers the classification of the reviews by using various ML algorithms. These are Na\u00efve Bayes (NB), Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Passive Aggressive (PA), Maximum Entropy (ME), Adaptive Boosting (AdaBoost), Multinomial NB (MNB), Bernoulli NB (BNB), Ridge Regression (RR) and Logistic Regression (LR). The performance of PA with a unigram is the best among other algorithms for all used datasets (IMDB, Cornell Movies, Amazon and Twitter) and provides values that range from 87% to 99.96% for all evaluation metrics.<\/jats:p>","DOI":"10.3390\/make1010014","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T03:36:41Z","timestamp":1544413001000},"page":"224-234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Analysis of Machine Learning Algorithms for Opinion Mining in Different Domains"],"prefix":"10.3390","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0740-3086","authenticated-orcid":false,"given":"Donia","family":"Gamal","sequence":"first","affiliation":[{"name":"Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt"}]},{"given":"Marco","family":"Alfonse","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt"}]},{"given":"El-Sayed","family":"M. El-Horbaty","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt"}]},{"given":"Abdel-Badeeh","family":"M. Salem","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gamal, D., Alfonse, M., El-Horbaty, E.S., and Salem, A.B. (2017, January 5\u20137). A comparative study on opinion mining algorithms of social media statuses. Proceedings of the Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/INTELCIS.2017.8260067"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Petz, G., Karpowicz, M., F\u00fcrschu\u00df, H., Auinger, A., St\u0159\u00edtesk\u00fd, V., and Holzinger, A. (2013). 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Proceedings of the International Conference on Data Mining Workshops (ICDMW), Atlantic City, NJ, USA.","DOI":"10.1109\/ICDMW.2015.7"}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:32:14Z","timestamp":1760196734000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,8]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["make1010014"],"URL":"https:\/\/doi.org\/10.3390\/make1010014","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,8]]}}}