{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T13:34:07Z","timestamp":1771335247810,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis.<\/jats:p>","DOI":"10.3390\/sym15051027","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T09:18:09Z","timestamp":1683278289000},"page":"1027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Innovations in Urdu Sentiment Analysis Using Machine and Deep Learning Techniques for Two-Class Classification of Symmetric Datasets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7189-816X","authenticated-orcid":false,"given":"Khalid Bin","family":"Muhammad","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Systems, Institute of Business Management Karachi, Karachi 75270, Pakistan"}]},{"given":"S. M. Aqil","family":"Burney","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Systems, Institute of Business Management Karachi, Karachi 75270, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"ref_1","first-page":"2009","article-title":"Top languages","volume":"11","author":"Weber","year":"2008","journal-title":"World"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tao, J., Zheng, F., Li, A., and Li, Y. (2009, January 10\u201312). Advances in Chinese Natural Language Processing and Language Resources. Proceedings of the 2009 Oriental COCOSDA International Conference on Speech Database and Assessments, Urumqi, China.","DOI":"10.1109\/ICSDA.2009.5278384"},{"key":"ref_3","unstructured":"Ahmad, W., and Edalati, M. (2022). Urdu Speech and Text Based Sentiment Analyzer. Comput. 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