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Various artificial intelligence technologies-based approaches on analysis of these opinions have emerged natural language processing in the name of different tasks. One of these tasks is Sentiment analysis, which is a popular method aiming the task of analyzing people\u2019s opinions which provides a powerful tool in making decisions for people, companies, governments, and researchers. It is desired to investigate the effect of using multi-layered and different neural networks together on the performance of the model to be developed in the sentiment analysis task. In this study, a new, deep learning-based model was proposed for sentiment analysis on IMDB movie reviews dataset. This model performs sentiment classification on vectorized reviews using two methods of Word2Vec, namely, the Skip Gram and Continuous Bag of Words, in three different vector sizes (100, 200, 300), with the help of 6 Bidirectional Gated Recurrent Units and 2 Convolution layers (MBi-GRUMCONV). In the experiments conducted with the proposed model, the dataset was split into 80%-20% and 70%-30% training-test sets, and 10% of the training splits were used for validation purposes. Accuracy and F1 score criteria were used to evaluate the classification performance. The 95.34% accuracy of the proposed model has outperformed the studies in the literature. As a result of the experiments, it was found that Skip Gram has a better contribution to classification success.\n<\/jats:p>","DOI":"10.1186\/s13677-022-00386-3","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T13:06:37Z","timestamp":1673355997000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["MBi-GRUMCONV: A novel Multi Bi-GRU and Multi CNN-Based deep learning model for social media sentiment analysis"],"prefix":"10.1186","volume":"12","author":[{"given":"Muhammet Sinan","family":"Ba\u015farslan","sequence":"first","affiliation":[]},{"given":"Fatih","family":"Kayaalp","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,10]]},"reference":[{"key":"386_CR1","unstructured":"\u201cGlobal social media statistics research summary\u00a02022.\u201d https:\/\/www.smartinsights.com\/social-media-marketing\/social-media-strategy\/new-global-social-media-research\/.\u00a0Accessed 20 Sept 2022"},{"key":"386_CR2","doi-asserted-by":"publisher","unstructured":"\u201cGround radar precipitation estimation with deep learning approaches in meteorological private,\u201d doi: https:\/\/doi.org\/10.1186\/s13677-020-00167-w.","DOI":"10.1186\/s13677-020-00167-w"},{"key":"386_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7216","author":"AT Kabakus","year":"2022","unstructured":"Kabakus AT, Erdogmus P (2022) An experimental comparison of the widely used pre-trained deep neural networks for image classification tasks towards revealing the promise of transfer-learning. 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