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Process."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.<\/jats:p>","DOI":"10.1145\/3638050","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T22:11:12Z","timestamp":1703715072000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4888-5018","authenticated-orcid":false,"given":"Asmaa Hashem","family":"Sweidan","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6553-4159","authenticated-orcid":false,"given":"Nashwa","family":"El-Bendary","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2362-1574","authenticated-orcid":false,"given":"Esraa","family":"Elhariri","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Fayoum University, Fayoum, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-3223-4_13"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.24017\/covid.8"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.langsci.2022.101482"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.07.006"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3127140"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2993967"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/info11060314"},{"key":"e_1_3_1_9_2","first-page":"8","article-title":"COVID-19 outbreak: Tweet based analysis and visualization towards the influence of coronavirus in the world","volume":"33","author":"Ra Muthusami","year":"2020","unstructured":"Muthusami Ra, Bharathi Ab, and Saritha Kc. 2020. 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