{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T08:54:43Z","timestamp":1764060883982,"version":"3.41.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T00:00:00Z","timestamp":1558396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hubei Provincial Natural Science Foundation of China","award":["2017CFA012"],"award-info":[{"award-number":["2017CFA012"]}]},{"name":"Key Technical Innovation Project of Hubei Provence of China","award":["2017AAA122"],"award-info":[{"award-number":["2017AAA122"]}]},{"name":"National Key Research and Development Program of China","award":["2017YFB1402203"],"award-info":[{"award-number":["2017YFB1402203"]}]},{"name":"Defense Industrial Technology Development Program","award":["JCKY2018110C165"],"award-info":[{"award-number":["JCKY2018110C165"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61601337"],"award-info":[{"award-number":["61601337"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2019,12,31]]},"abstract":"<jats:p>With the advent of social network services, Arabs\u2019 opinions on the web have attracted many researchers in recent years toward detecting and classifying sentiments in Arabic tweets and reviews. However, the impact of word embeddings vectors (WEVs) initialization and dataset balance on Arabic sentiment classification using deep learning has not been thoroughly studied. In this article, a multi-channel embedding convolutional neural network (MCE-CNN) is proposed to improve Arabic sentiment classification by learning sentiment features from different text domains, word, and character n-grams levels. MCE-CNN encodes a combination of different pre-trained word embeddings into the embedding block at each embedding channel and trains these channels in parallel. Besides, a separate feature extraction module implemented in a CNN block is used to extract more relevant sentiment features. These channels and blocks help to start training on high-quality WEVs and fine-tuning them. The performance of MCE-CNN is evaluated on several standard balanced and imbalanced datasets to reflect real-world use cases. Experimental results show that MCE-CNN provides a high classification accuracy and benefits from the second embedding channel on both standard Arabic and dialectal Arabic text, which outperforms state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3314941","type":"journal-article","created":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T18:01:38Z","timestamp":1558634498000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Multi-Channel Embedding Convolutional Neural Network Model for Arabic Sentiment Classification"],"prefix":"10.1145","volume":"18","author":[{"given":"Abdelghani","family":"Dahou","sequence":"first","affiliation":[{"name":"Wuhan University of Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengwu","family":"Xiong","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Abd","family":"Elaziz","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, Hubei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,5,21]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1109\/AEECT.2013.6716448"},{"volume-title":"Proceedings of the International Conference on Neural Information Processing. 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