{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T13:23:52Z","timestamp":1771075432423,"version":"3.50.1"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T00:00:00Z","timestamp":1499904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2017,12,31]]},"abstract":"<jats:p>\n            While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose\n            <jats:italic>A<\/jats:italic>\n            <jats:italic>R<\/jats:italic>\n            ecursive Deep Learning Model for\n            <jats:italic>O<\/jats:italic>\n            pinion\n            <jats:italic>M<\/jats:italic>\n            ining in\n            <jats:italic>A<\/jats:italic>\n            rabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.\n          <\/jats:p>","DOI":"10.1145\/3086575","type":"journal-article","created":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T14:29:57Z","timestamp":1499956197000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":54,"title":["AROMA"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6616-1446","authenticated-orcid":false,"given":"Ahmad","family":"Al-Sallab","sequence":"first","affiliation":[{"name":"Electronics and Communications Dpt., Faculty of Engineering, Cairo University, Cairo, Egypt"}]},{"given":"Ramy","family":"Baly","sequence":"additional","affiliation":[{"name":"American University of Beirut, Beirut, Lebanon"}]},{"given":"Hazem","family":"Hajj","sequence":"additional","affiliation":[{"name":"American University of Beirut, Beirut, Lebanon"}]},{"given":"Khaled Bashir","family":"Shaban","sequence":"additional","affiliation":[{"name":"Qatar University, Doha, Qatar"}]},{"given":"Wassim","family":"El-Hajj","sequence":"additional","affiliation":[{"name":"American University of Beirut, Beirut, Lebanon"}]},{"given":"Gilbert","family":"Badaro","sequence":"additional","affiliation":[{"name":"American University of Beirut, Beirut, Lebanon"}]}],"member":"320","published-online":{"date-parts":[[2017,7,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1361684.1361685"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.110"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the International Conference on Language Resources and Evaluation (LREC\u201914)","author":"Abdul-Mageed Muhammad"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2002736.2002851"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.11.007"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 2013 8th International Conference for Internet Technology and Secured Transactions (ICITST\u201913)","author":"Al-Kabi Mohammed N.","year":"2013"},{"key":"e_1_2_1_7_1","volume-title":"ANLP Workshop 2015","author":"Al Sallab Ahmad A.","year":"2015"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-013-0665-3"},{"key":"e_1_2_1_9_1","volume-title":"Atiya","author":"Aly Mohamed A.","year":"2013"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-3203"},{"key":"e_1_2_1_11_1","first-page":"165","article-title":"A large scale arabic sentiment lexicon for arabic opinion mining","volume":"2014","author":"Badaro Gilbert","year":"2014","journal-title":"ANLP"},{"key":"e_1_2_1_12_1","volume-title":"Neural Networks: Tricks of the Trade","author":"Bengio Yoshua"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 3rd International WordNet Conference. 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