{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:41:50Z","timestamp":1782862910510,"version":"3.54.5"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Machine learning based sentiment analysis is an interdisciplinary approach in opinion mining, particularly in the field of media and communication research. In spite of their different backgrounds, researchers have collaborated to test, train and again retest the machine learning approach to collect, analyse and withdraw a meaningful insight from large datasets. This research classifies the texts of micro-blog (tweets) into positive and negative responses about a particular phenomenon. The study also demonstrates the process of compilation of corpus for review of sentiments, cleaning the body of text to make it a meaningful text, find people\u2019s emotions about it, and interpret the findings. Till date the public sentiment after abrogation of Article 370 has not been studied, which adds the novelty to this scientific study. This study includes the dataset collection from Twitter that comprises 66.7 % of positive tweets and 34.3 % of negative tweets of the people about the abrogation of Article 370. Experimental testing reveals that the proposed methodology is much more effective than the previously proposed methodology. This study focuses on comparison of unsupervised lexicon-based models (TextBlob, AFINN, Vader Sentiment) and supervised machine learning models (KNN, SVM, Random Forest and Na\u00efve Bayes) for sentiment analysis. This is the first study with cyber public opinion over the abrogation of Article 370. Twitter data of more than 2 lakh tweets were collected by the authors. After cleaning, 29732 tweets were selected for analysis. As per the results among supervised learning, Random Forest performs the best, whereas among unsupervised learning TextBlob achieves the highest accuracy of 99 % and 88 %, respectively. Performance parameters of the proposed supervised machine learning models also surpass the result of the recent study performed in 2023 for sentiment analysis.<\/jats:p>","DOI":"10.2478\/acss-2023-0012","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T06:47:53Z","timestamp":1692341273000},"page":"125-136","source":"Crossref","is-referenced-by-count":11,"title":["Empirical Analysis of Supervised and Unsupervised Machine Learning Algorithms with Aspect-Based Sentiment Analysis"],"prefix":"10.2478","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8689-9878","authenticated-orcid":false,"given":"Satwinder","family":"Singh","sequence":"first","affiliation":[{"name":"Department of CST , Central University of Punjab , Bathinda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harpreet","family":"Kaur","sequence":"additional","affiliation":[{"name":"Department of CST , Central University of Punjab , Bathinda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2890-1244","authenticated-orcid":false,"given":"Rubal","family":"Kanozia","sequence":"additional","affiliation":[{"name":"Department of Mass Communications & Journalism , Central University of Punjab , Bathinda"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gurpreet","family":"Kaur","sequence":"additional","affiliation":[{"name":"Faculty of Law , Guru Kashi University , Talwandi Sabo , Bathinda"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"2026042709093793959_j_acss-2023-0012_ref_001","unstructured":"The Hindu, \u201cAbrogation of Article 370 led to breakdown of law and order in J&K,\u201d 2020. [Online]. Available: https:\/\/www.thehindu.com\/news\/cities\/Visakhapatnam\/abrogation-of-article-370-led-to-breakdown-of-law-and-order-in-jk\/article30669954.ece. (Accessed on: 26 June 2020)."},{"key":"2026042709093793959_j_acss-2023-0012_ref_002","unstructured":"S. Bhat, \u201cJ&K administration ends house arrest of political leaders in Jammu,\u201d Feb. 2022. [Online]. Available: https:\/\/www.indiatoday.in\/india\/story\/j-k-administration-ends-house-arrest-of-political-leaders-in-jammu-1605412-2019-10-02"},{"key":"2026042709093793959_j_acss-2023-0012_ref_003","unstructured":"The Hindu, \u201cLeft parties protest amendment to Article 370, vow to continue fighting,\u201d Aug. 2019. [Online]. Available: https:\/\/www.thehindu.com\/news\/national\/left-parties-protest-scrapping-of-article-370-vow-to-continue-the-fight\/article28825167.ece."},{"key":"2026042709093793959_j_acss-2023-0012_ref_004","doi-asserted-by":"crossref","unstructured":"Y. Dang, Y. Zhang, and H. Chen, \u201cA lexicon-enhanced method for sentiment classification,\u201d IEEE Intell. Syst., vol .25, no. 4, pp. 46\u201353, Nov. 2010. https:\/\/doi.org\/10.1109\/MIS.2009.105","DOI":"10.1109\/MIS.2009.105"},{"key":"2026042709093793959_j_acss-2023-0012_ref_005","doi-asserted-by":"crossref","unstructured":"M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, \u201cLexicon-based methods for sentiment analysis,\u201d Comput. Linguist., vol. 37, no. 2, pp. 267\u2013307, Jun. 2011. https:\/\/doi.org\/10.1162\/COLI_a_00049","DOI":"10.1162\/COLI_a_00049"},{"key":"2026042709093793959_j_acss-2023-0012_ref_006","unstructured":"\u201cAFINN sentiment lexicon.\u201d [Online]. Available: http:\/\/corpustext.com\/reference\/sentiment_afinn.html."},{"key":"2026042709093793959_j_acss-2023-0012_ref_007","unstructured":"P. Pandey, \u201cSimplifying sentiment analysis using VADER in Python (on social media text),\u201d Sep. 2018. [Online]. Available: https:\/\/medium.com\/analytics-vidhya\/simplifyingsocial-media-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f. (Accessed on: 24 March, 2020)."},{"key":"2026042709093793959_j_acss-2023-0012_ref_008","unstructured":"S. Mishra, \u201cUnsupervised learning and data clustering,\u201d May 2017. [Online]. Available: https:\/\/towardsdatascience.com\/unsupervised-learning-and-data-clustering-eeecb78b422a. (Accessed on: 24 March, 2020)."},{"key":"2026042709093793959_j_acss-2023-0012_ref_009","doi-asserted-by":"crossref","unstructured":"A. Khatua, K. Ghosh, and N. Chaki, \u201cCan#Twitter_Trends predict election results? Evidence from 2014 Indian General Election,\u201d in 48th Hawaii International Conference on System Sciences, Kauai, HI, USA, Jan. 2015, pp. 1676\u20131685. https:\/\/doi.org\/10.1109\/HICSS.2015.202","DOI":"10.1109\/HICSS.2015.202"},{"key":"2026042709093793959_j_acss-2023-0012_ref_010","doi-asserted-by":"crossref","unstructured":"L. K. Hansen, A. Arvidsson, F. A. Nielsen, E. Colleoni, and M. Etter, \u201cGood friends, bad news: Affect and virality in Twitter,\u201d in Future Information Technology. Communications in Computer and Information Science, J. H. Park, L. T. Yang, and C. Lee, Eds., vol. 185. Springer, Berlin, Heidelberg, 2011, pp. 34\u201343. https:\/\/doi.org\/10.1007\/978-3-642-22309-9_5","DOI":"10.1007\/978-3-642-22309-9_5"},{"key":"2026042709093793959_j_acss-2023-0012_ref_011","doi-asserted-by":"crossref","unstructured":"M. Hu and B. Liu, \u201cMining and summarizing customer reviews,\u201d in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, Aug. 2004, pp. 168\u2013177. https:\/\/doi.org\/10.1145\/1014052.1014073","DOI":"10.1145\/1014052.1014073"},{"key":"2026042709093793959_j_acss-2023-0012_ref_012","doi-asserted-by":"crossref","unstructured":"R. Prabowo and M. 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Rao, \u201cFine-grained sentiment analysis in Python (Part-1),\u201d Towards Data Science, Sep. 2019. [Online]. Available: https:\/\/towardsdatascience.com\/fine-grained-sentiment-analysis-in-python-part-1-2697bb111ed4"},{"key":"2026042709093793959_j_acss-2023-0012_ref_024","unstructured":"Developer Platform, \u201cTwitter Rest API. [Online]. Available: https:\/\/developer.twitter.com\/en\/search-results?limit=10&offset=0&q=Twitter%20Rest%20API&searchPath=%2Fcontent%2Fdeveloper-twitter%2Fen&sort=relevance."},{"key":"2026042709093793959_j_acss-2023-0012_ref_025","doi-asserted-by":"crossref","unstructured":"P. Turney, \u201cThumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews,\u201d in Meeting on Association for Computational Linguistics, Philadelphia, USA, Jul. 2002, pp. 417\u2013424. https:\/\/arxiv.org\/ftp\/cs\/papers\/0212\/0212032.pdf","DOI":"10.3115\/1073083.1073153"},{"key":"2026042709093793959_j_acss-2023-0012_ref_026","unstructured":"J. Brownlee, \u201cWhat is a confusion matrix in machine learning,\u201d Aug. 2020. [Online]. Available: https:\/\/machinelearningmastery.com\/confusion-matrix-machine-learning\/"},{"key":"2026042709093793959_j_acss-2023-0012_ref_027","doi-asserted-by":"crossref","unstructured":"N. Al Shammari and A. Al Mansour, \u201cAspect-based sentiment analysis and location detection for Arabic language Tweets,\u201d Applied Computer Systems, vol. 27, no. 2, pp. 119\u2013127, Dec. 2022. https:\/\/doi.org\/10.2478\/acss-2022-0013","DOI":"10.2478\/acss-2022-0013"}],"container-title":["Applied Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/acss-2023-0012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:29:56Z","timestamp":1777289396000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/acss-2023-0012"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,8,17]]},"published-print":{"date-parts":[[2023,6,1]]}},"alternative-id":["10.2478\/acss-2023-0012"],"URL":"https:\/\/doi.org\/10.2478\/acss-2023-0012","relation":{},"ISSN":["2255-8691"],"issn-type":[{"value":"2255-8691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}