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Arabic Sentiment Analysis presents a challenge undertaking due to its complexity, ambiguity, various dialects, the scarcity of resources, the morphological richness of the language, the absence of contextual information, and the absence of explicit sentiment words in an implicit piece of text. Recently, deep learning has obviously shown a great success in the field of sentiment analysis and is considered as the state-of-the-art model in Arabic Sentiment Analysis. However, the state-of-the-art accuracy for Arabic sentiment analysis still needs improvements regarding contextual information and implicit sentiment expressed in different real cases. In this paper, an efficient Bidirectional LSTM Network (BiLSTM) is investigated to enhance Arabic Sentiment Analysis, by applying Forward-Backward encapsulate contextual information from Arabic feature sequences. The experimental results on six benchmark sentiment analysis datasets demonstrate that our model achieves significant improvements over the state-of-art deep learning models and the baseline traditional machine learning methods.<\/jats:p>","DOI":"10.1515\/jisys-2020-0021","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T22:09:30Z","timestamp":1610575770000},"page":"395-412","source":"Crossref","is-referenced-by-count":63,"title":["Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text"],"prefix":"10.1515","volume":"30","author":[{"given":"Hanane","family":"Elfaik","sequence":"first","affiliation":[{"name":"LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, Sidi Mohamed Ben Abdellah University , Fez , Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"El Habib","family":"Nfaoui","sequence":"additional","affiliation":[{"name":"LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, Sidi Mohamed Ben Abdellah University , Fez , Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"2025120523322312661_j_jisys-2020-0021_ref_001","doi-asserted-by":"crossref","unstructured":"M. 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