{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T20:45:16Z","timestamp":1774903516951,"version":"3.50.1"},"reference-count":88,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Sentiment analysis aims to automatically classify the subject\u2019s sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as a topic, product, movie, news, etc. Deep learning has recently emerged as a powerful machine learning technique to tackle the growing demand for accurate sentiment analysis. However, the majority of research efforts are devoted to English-language only, while information of great importance is also available in other languages. This paper presents a novel, context-aware, deep-learning-driven, Persian sentiment analysis approach. Specifically, the proposed deep-learning-driven automated feature-engineering approach classifies Persian movie reviews as having positive or negative sentiments. Two deep learning algorithms, convolutional neural networks (CNN) and long-short-term memory (LSTM), are applied and compared with our previously proposed manual-feature-engineering-driven, SVM-based approach. Simulation results demonstrate that LSTM obtained a better performance as compared to multilayer perceptron (MLP), autoencoder, support vector machine (SVM), logistic regression and CNN algorithms.<\/jats:p>","DOI":"10.3390\/e23050596","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T10:59:12Z","timestamp":1620817152000},"page":"596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Sentiment Analysis of Persian Movie Reviews Using Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Kia","family":"Dashtipour","sequence":"first","affiliation":[{"name":"Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK"}]},{"given":"Mandar","family":"Gogate","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK"}]},{"given":"Ahsan","family":"Adeel","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton WV1 1LY, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6826-207X","authenticated-orcid":false,"given":"Hadi","family":"Larijani","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1609\/aaai.v32i1.11559","article-title":"SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings","volume":"32","author":"Cambria","year":"2018","journal-title":"AAAI"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s12559-016-9415-7","article-title":"Multilingual sentiment analysis: State of the art and independent comparison of techniques","volume":"8","author":"Dashtipour","year":"2016","journal-title":"Cogn. 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