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Due to COVID-19, coronavirus information disseminated on social media affects people\u2019s lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people\u2019s feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model. We also compare the proposed method to various deep learning models: CNN, LSTM, and BiLSTM. Experiments are performed on three different Arabic datasets related to COVID-19 and vaccines. Empirical findings show that the proposed model outperformed the other models\u2019 results by achieving F-measures of 76.76%, 87.%, and 80.5% on the SenWave, AraCOVID19-SSD, and ArCovidVac datasets, respectively.<\/jats:p>","DOI":"10.1186\/s13677-024-00644-6","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T11:02:03Z","timestamp":1712660523000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets"],"prefix":"10.1186","volume":"13","author":[{"given":"Naglaa","family":"Abdelhady","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taysir","family":"Hassan\u00a0A.\u00a0Soliman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"F.\u00a0Farghally","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"issue":"1","key":"644_CR1","first-page":"1","volume":"5","author":"B Liu","year":"2012","unstructured":"Liu B (2012) Sentiment analysis and opinion mining. 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