{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:40:37Z","timestamp":1775745637417,"version":"3.50.1"},"reference-count":95,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Education and Science of the Republic Kazakhstan","award":["AP09259309"],"award-info":[{"award-number":["AP09259309"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study\u2019s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.<\/jats:p>","DOI":"10.3390\/sym15030645","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T05:18:43Z","timestamp":1678079923000},"page":"645","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6210-4487","authenticated-orcid":false,"given":"Kanwal","family":"Ahmed","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1536-2285","authenticated-orcid":false,"given":"Muhammad Imran","family":"Nadeem","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyun","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0211-5461","authenticated-orcid":false,"given":"Nouf","family":"Al-Kahtani","sequence":"additional","affiliation":[{"name":"Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-5267","authenticated-orcid":false,"given":"Hend Khalid","family":"Alkahtani","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9234-5898","authenticated-orcid":false,"given":"Samih M.","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8318-3794","authenticated-orcid":false,"given":"Orken","family":"Mamyrbayev","sequence":"additional","affiliation":[{"name":"Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1007\/s12559-021-09833-w","article-title":"Automatically building financial sentiment lexicons while accounting for negation","volume":"14","author":"Bos","year":"2022","journal-title":"Cognit. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ahmed, K., Nadeem, M.I., Li, D., Zheng, Z., Ghadi, Y.Y., Assam, M., and Mohamed, H.G. (2022). Exploiting Stacked Autoencoders for Improved Sentiment Analysis. Appl. Sci., 12.","DOI":"10.3390\/app122312380"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, D., Ahmed, K., Zheng, Z., Mohsan, S.A.H., Alsharif, M.H., Hadjouni, M., Jamjoom, M.M., and Mostafa, S.M. (2022). Roman Urdu Sentiment Analysis Using Transfer Learning. Appl. Sci., 12.","DOI":"10.3390\/app122010344"},{"key":"ref_4","unstructured":"Britannica (2023, February 20). The Editors of Encyclopaedia. \u201cUrdu Language\u201d. Encyclopedia Britannica, 20 October 2022. Available online: https:\/\/www.britannica.com\/topic\/Urdu-language."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e12397","DOI":"10.1111\/exsy.12397","article-title":"Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource-poor language","volume":"36","author":"Asghar","year":"2019","journal-title":"Expert Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1108\/LHT-03-2018-0036","article-title":"Scientific collaboration networks in Pakistan and their impact on institutional research performance: A case study based on Scopus publications","volume":"37","author":"Sabah","year":"2019","journal-title":"Libr. Hi Tech"},{"key":"ref_7","first-page":"1","article-title":"Native language identification of fluent and advanced non-native writers","volume":"19","author":"Sarwar","year":"2020","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP)"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"50030","DOI":"10.1109\/ACCESS.2018.2869198","article-title":"An effective and scalable framework for authorship attribution query processing","volume":"6","author":"Sarwar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bibi, R., Qamar, U., Ansar, M., and Shaheen, A. (2019, January 29\u201331). Sentiment analysis for Urdu news tweets using decision tree. Proceedings of the 2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA), Honolulu, HI, USA.","DOI":"10.1109\/SERA.2019.8886788"},{"key":"ref_10","unstructured":"(2021, July 19). NLPL Word Embeddings Repository. Available online: http:\/\/vectors.nlpl.eu\/repository\/."},{"key":"ref_11","unstructured":"Humayoun, M., Hammarstr\u00f6m, H., and Ranta, A. (2007, January 7\u201311). Implementing Urdu Grammar as Open Source Software. Proceedings of the Conference on Language and Technology, Khyber Pakhtunkhwa."},{"key":"ref_12","unstructured":"Humayoun, M., and Akhtar, N. (2021). Intelligent Systems with Applications, Elsevier."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kiritchenko, S., Mohammad, S., and Salameh, M. (2016, January 16\u201317). SemEval-2016 task 7: Determining sentiment intensity of english and arabic phrases. Proceedings of the 10th international workshop on semantic evaluation (SEMEVAL-2016), San Diego, CA, USA.","DOI":"10.18653\/v1\/S16-1004"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Villena-Rom\u00e1n, J., Garc\u00eda-Morera, J., and Gonz\u00e1lez-Crist\u00f3bal, J.C. (2014, January 23\u201324). DAEDALUS at SemEval-2014 task 9: Comparing approaches for sentiment analysis in Twitter. Proceedings of the 8th International Workshop Semantic Eval. (SemEval), Dublin, Ireland.","DOI":"10.3115\/v1\/S14-2035"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nadeem, M.I., Ahmed, K., Li, D., Zheng, Z., Alkahtani, H.K., Mostafa, S.M., Mamyrbayev, O., and Abdel Hameed, H. (2023). EFND: A Semantic, Visual, and Socially Augmented Deep Framework for Extreme Fake News Detection. Sustainability, 15.","DOI":"10.3390\/su15010133"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nadeem, M.I., Ahmed, K., Li, D., Zheng, Z., Naheed, H., Muaad, A.Y., Alqarafi, A., and Abdel Hameed, H. (2023). SHO-CNN: A Metaheuristic Optimization of a Convolutional Neural Network for Multi-Label News Classification. Electronics, 12.","DOI":"10.3390\/electronics12010113"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nadeem, M.I., Mohsan, S.A.H., Ahmed, K., Li, D., Zheng, Z., Shafiq, M., Karim, F.K., and Mostafa, S.M. (2023). HyproBert: A Fake News Detection Model Based on Deep Hypercontext. Symmetry, 15.","DOI":"10.3390\/sym15020296"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3300050","article-title":"Role of discourse information in Urdu sentiment classification: A rule-based method and machine-learning technique","volume":"18","author":"Awais","year":"2019","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.eij.2020.04.003","article-title":"A survey on sentiment analysis in Urdu: A resource-poor language","volume":"22","author":"Khattak","year":"2021","journal-title":"Egypt. Inform. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chauhan, U.A., Afzal, M.T., Shahid, A., Abdar, M., Basiri, M.E., and Zhou, X. (2020). A Comprehensive Analysis of Adverb Types for Mining User Sentiments on Amazon Product Reviews, World Wide Web.","DOI":"10.1007\/s11280-020-00785-z"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Poria, S., Chaturvedi, I., Cambria, E., and Bisio, F. (2016, January 24\u201329). Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727784"},{"key":"ref_22","first-page":"26","article-title":"Words are important: Improving sentiment analysis in the persian language by lexicon refining","volume":"17","author":"Basiri","year":"2018","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process. (TALLIP)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Cambridge University Press.","DOI":"10.1017\/CBO9781139084789"},{"key":"ref_24","first-page":"154","article-title":"Lexicon-based sentiment analysis in Persian","volume":"1","author":"Basiri","year":"2017","journal-title":"Curr. Future Dev. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1177\/0165551519827886","article-title":"HOMPer: A new hybrid system for opinion mining in the Persian language","volume":"46","author":"Basiri","year":"2020","journal-title":"J. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cambria, E., Li, Y., Xing, F., Poria, S., and Kwok, K. (2020, January 19\u201323). SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Virtual, Ireland.","DOI":"10.1145\/3340531.3412003"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109781","DOI":"10.1016\/j.rser.2020.109781","article-title":"Energy choices in Alaska: Mining people\u2019s perception and attitudes from geotagged tweets","volume":"124","author":"Abdar","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_28","first-page":"2011","article-title":"Combining Lexicon-Based and Learning-Based Methods for Twitter Sentiment Analysis","volume":"89","author":"Zhang","year":"2011","journal-title":"Lab. Tech. Rep.-Hpl-2011"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mudinas, A., Zhang, D., and Levene, M. (2012, January 12). Combining lexicon and learning based approaches for concept-level sentiment analysis. Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, ACM, Beijing, China.","DOI":"10.1145\/2346676.2346681"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.eswa.2018.04.006","article-title":"A domain transferable lexicon set for twitter sentiment analysis using a supervised machine learning approach","volume":"106","author":"Ghiassi","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_31","unstructured":"Chikersal, P., Poria, S., Cambria, E., Gelbukh, A., and Siong, C.E. (2015). Computational Linguistics and Intelligent Text Processing, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.dss.2014.10.004","article-title":"Sentiment analysis: Bayesian ensemble learning","volume":"68","author":"Fersini","year":"2014","journal-title":"Decis. Support Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.engappai.2016.01.012","article-title":"Recognizing emotions in text using ensemble of classifiers","volume":"51","author":"Perikos","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_34","unstructured":"Chalothom, T., and Ellman, J. (2015). Information Science and Applications, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Prusa, J., Khoshgoftaar, T.M., and Dittman, D.J. (2015, January 13\u201315). Using ensemble learners to improve classifier performance on tweet sentiment data. Proceedings of the 2015 IEEE International Conference on Information Reuse and Integration, San Francisco, CA, USA.","DOI":"10.1109\/IRI.2015.49"},{"key":"ref_36","first-page":"2009","article-title":"Twitter sentiment classification using distant supervision","volume":"1","author":"Go","year":"2009","journal-title":"CS224N Proj. Rep. Stanf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jameel, S., Bouraoui, Z., and Schockaert, S. (2018, January 15\u201320). Unsupervised learning of distributional relation vectors. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1003"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.ipm.2018.12.005","article-title":"Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean","volume":"56","author":"Song","year":"2019","journal-title":"Inf. Process. Manage."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sharma, R., Somani, A., Kumar, L., and Bhattacharyya, P. (2017, January 7\u201311). Sentiment intensity ranking among adjectives using sentiment bearing word embeddings. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1058"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1016\/j.neucom.2017.11.023","article-title":"Towards twitter sentiment classification by multi-level sentiment-enriched word embeddings","volume":"275","author":"Xiong","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102484","DOI":"10.1016\/j.ipm.2020.102484","article-title":"Deep transfer learning baselines for sentiment analysis in russian","volume":"58","author":"Smetanin","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"102233","DOI":"10.1016\/j.ipm.2020.102233","article-title":"Deep sentiments in roman urdu text using recurrent convolutional neural network model","volume":"57","author":"Mahmood","year":"2020","journal-title":"Inf Process. Manag."},{"key":"ref_44","unstructured":"Huang, M., Cao, Y., and Dong, C. (2016). Modeling rich contexts for sentiment classification with lstm. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.procs.2017.10.118","article-title":"Comparative evaluation of sentiment analysis methods across arabic dialects","volume":"117","author":"Baly","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","unstructured":"Akhtar, M.S., Ghosal, D., Ekbal, A., Bhattacharyya, P., and Kurohashi, S. (2018). A multitask ensemble framework for emotion, sentiment and intensity prediction. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.procs.2018.10.466","article-title":"Sentiment analysis of arabic tweets using deep learning","volume":"142","author":"Heikal","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Nabil, M., Aly, M., and Atiya, A. (2015, January 17\u201321). Astd: Arabic sentiment tweets dataset. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1299"},{"key":"ref_49","unstructured":"Minaee, S., Azimi, E., and Abdolrashidi, A. (2019). Deep-sentiment: Sentiment analysis using ensemble of cnn and bi-lstm models. arXiv."},{"key":"ref_50","unstructured":"M\u00fcller, M., Salath\u00e9, M., and Kummervold, P.E. (2020). Covid-twitter-bert: A natural language processing model to analyse COVID-19 content on twitter. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Syed, A.Z., Aslam, M., and Martinez-Enriquez, A.M. (2010, January 8\u201313). Lexicon based sentiment analysis of Urdu text using SentiUnits, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Proceedings of the 9th Mexican International Conference on Artificial Intelligence, MICAI 2010, Pachuca, Mexico.","DOI":"10.1007\/978-3-642-16761-4_4"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10462-012-9322-6","article-title":"Associating targets with SentiUnits: A step forward in sentiment analysis of Urdu text","volume":"41","author":"Syed","year":"2014","journal-title":"Artif. Intell. Rev."},{"key":"ref_53","unstructured":"Syed, A.Z., Martinez-Enriquez, A.M., Nazir, A., Aslam, M., and Basit, R.H. (2017, January 21\u201324). Mining the Urdu language-based web content for opinion extraction, in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Proceedings of the Pattern Recognition: 9th Mexican Conference, MCPR 2017, Huatulco, Mexico."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1016\/j.tele.2018.08.003","article-title":"Lexicon-based approach outperforms supervised machine learning approach for Urdu sentiment analysis in multiple domains","volume":"35","author":"Mukhtar","year":"2018","journal-title":"Telemat. Informat."},{"key":"ref_55","first-page":"21","article-title":"Opinion within opinion: Segmentation approach for Urdu sentiment analysis","volume":"15","author":"Hassan","year":"2018","journal-title":"Int. Arab J. Inf. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1007\/s12559-017-9481-5","article-title":"Effective use of evaluation measures for the validation of best classifier in Urdu sentiment analysis","volume":"9","author":"Mukhtar","year":"2017","journal-title":"Cognit. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1851001","DOI":"10.1142\/S0218001418510011","article-title":"Urdu sentiment analysis using supervised machine learning approach","volume":"32","author":"Mukhtar","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_58","first-page":"269","article-title":"Sentiment analysis on Urdu tweets using Markov chains","volume":"1","author":"Nasim","year":"2020","journal-title":"Social Netw. Comput. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1080\/17517575.2020.1755455","article-title":"Exploring deep learning approaches for Urdu text classification in product manufacturing","volume":"16","author":"Akhter","year":"2022","journal-title":"Enterp. Inf. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.procs.2019.01.202","article-title":"Deep learning-based sentiment analysis for Roman Urdu text","volume":"147","author":"Ghulam","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_61","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_62","unstructured":"Riaz, K. (2007). BCS IRSG Symposium: Future Directions in Information Access, Association for Computing Machinery."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Khan, I.U., Khan, A., Khan, W., Su\u2019ud, M.M., Alam, M.M., Subhan, F., and Asghar, M.Z. (2022). A review of Urdu sentiment analysis with multilingual perspective: A case of Urdu and roman Urdu language. Computers, 11.","DOI":"10.3390\/computers11010003"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e1032","DOI":"10.7717\/peerj-cs.1032","article-title":"Sentiment analysis techniques, challenges, and opportunities: Urdu language-based analytical study","volume":"8","author":"Liaqat","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/A:1019956318069","article-title":"A perspective view and survey of meta-learning","volume":"18","author":"Vilalta","year":"2002","journal-title":"Artif. Intell. Rev."},{"key":"ref_66","first-page":"81","article-title":"Meta-learning in distributed data mining systems: Issues and approaches","volume":"3","author":"Prodromidis","year":"2000","journal-title":"Adv. Distrib. Parallel Knowl. Discov."},{"key":"ref_67","first-page":"90","article-title":"Manning, Baselines and bigrams: Simple, good sentiment and topic classification","volume":"Volume 2","author":"Wang","year":"2012","journal-title":"Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers"},{"key":"ref_68","unstructured":"Jabreel, M., Hassan, F., and Moreno, A. (2018). Advances in Hybridization of Intelligent Methods, Springer."},{"key":"ref_69","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). Distilbert a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv."},{"key":"ref_70","unstructured":"Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019, January 2\u20137). Bert: Pretraining of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MI, USA."},{"key":"ref_71","unstructured":"Zia, H.B., Raza, A.A., and Athar, A. (2018, January 21\u201325). Urdu word segmentation using conditional random fields (CRFs). Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA. Available online: http:\/\/aclweb.org\/anthology\/C18-1217."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Akram, Q.-u.-A., Naseer, A., and Hussain, S. (2009, January 6\u20137). Assas-band, an affix-exception-list based Urdu stemmer. Proceedings of the 7th Workshop on Asian Language Resources, Singapore.","DOI":"10.3115\/1690299.1690305"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Alam, M., and Hussain, S. (2017, January 24\u201326). Sequence to sequence networks for Roman-Urdu to Urdu transliteration. Proceedings of the 2017 International Multi-Topic Conference (INMIC), Lahore, Pakistan.","DOI":"10.1109\/INMIC.2017.8289449"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Khan, M., and Malik, K. (2018, January 5\u20136). Sentiment classification of customer\u2019s reviews about automobiles in roman urdu. Proceedings of the Future of Information and Communication Conference, Cham, Switzerland.","DOI":"10.1007\/978-3-030-03405-4_44"},{"key":"ref_75","unstructured":"Silic, A., Chauchat, J.-H., Basic, B.D., and Morin, A. (2007, January 3\u20137). N-grams and morphological normalization in text classification: A comparison on a croatian-english parallel corpus. Proceedings of the Portuguese Conference on Artificial Intelligence, Guimar\u00e3es, Portugal."},{"key":"ref_76","unstructured":"Liu, B. (2007). Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s11192-018-2767-x","article-title":"A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis","volume":"116","author":"Hassan","year":"2018","journal-title":"Scientometrics"},{"key":"ref_78","unstructured":"Adeeba, F., Akram, Q., Khalid, H., and Hussain, S. (2014, January 13\u201315). Cle urdu books n-grams. Proceedings of the Conference on language and technology, Center for Language Engineering, Karachi, Pakistan."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_80","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. arXiv."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s12559-014-9298-4","article-title":"Word polarity disambiguation using bayesian model and opinion-level features","volume":"7","author":"Xia","year":"2015","journal-title":"Cogn. Comput."},{"key":"ref_82","unstructured":"Armand, J., Grave, E., Bojanowski, P., and Mikolov, T. (2017, January 3\u20137). Bag of tricks for efficient text classification. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain."},{"key":"ref_83","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1023\/A:1007413511361","article-title":"On the optimality of the simple Bayesian classifier under zero-one loss","volume":"29","author":"Domingos","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_87","unstructured":"Kleinbaum, D.G., Dietz, K., Gail, M., Klein, M., and Klein, M. (2002). Logistic Regression, Springer."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_89","first-page":"535","article-title":"Generating accurate and diverse members of a neural-network ensemble","volume":"8","author":"Opitz","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e12751","DOI":"10.1111\/exsy.12751","article-title":"Sentiment analysis for Urdu online reviews using deep learning models","volume":"38","author":"Safder","year":"2021","journal-title":"Expert Syst."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"97803","DOI":"10.1109\/ACCESS.2021.3093078","article-title":"Urdu sentiment analysis with deep learning methods","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"5436","DOI":"10.1038\/s41598-022-09381-9","article-title":"Multi-class sentiment analysis of urdu text using multilingual BERT","volume":"12","author":"Khan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1016\/j.jacr.2018.02.026","article-title":"The pareto principle","volume":"15","author":"Harvey","year":"2018","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Dong, Y.-S., and Han, K.-S. (2005, January 13\u201317). Text classification based on data partitioning and parameter varying ensembles. Proceedings of the 2005 ACM Symposium on Applied Computing, Santa Fe, NM, USA.","DOI":"10.1145\/1066677.1066916"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s00362-012-0443-4","article-title":"A generalization of the wilcoxon signed-rank test and its applications","volume":"54","author":"Taheri","year":"2013","journal-title":"Statist. Pap."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/3\/645\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:17Z","timestamp":1760122037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/3\/645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,3]]},"references-count":95,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["sym15030645"],"URL":"https:\/\/doi.org\/10.3390\/sym15030645","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,3]]}}}