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Our framework uses a nonparametric approach to construct neighborhoods of related texts based on Jaccard similarities. Then, a new deep recurrent neural network architecture is proposed, comprising two distinct modules: bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). The proposed model aims to effectively capture informative features from the input text and its neighbors. The result of each module is processed through the maximum operation, which selects the most pertinent data. Finally, the extracted features are concatenated and subjected to classification to achieve accurate sentiment prediction. Previous studies have commonly employed a parametric approach to represent textual metadata. However, our approach utilizes a nonparametric approach, enabling our model to perform strongly even when the text vocabulary varies between training and testing. The proposed DTSC model has been evaluated on five real-world sentiment datasets, achieving 99.60% accuracy on the Binary_Getty (BG) dataset, 98.32% accuracy on the Binary_iStock (BIS) dataset, 96.13% accuracy on Twitter, 82.19% accuracy on the multi-view sentiment analysis (MVSA) dataset, and 87.60% accuracy on the IMDB dataset. These findings demonstrate that the proposed model outperforms established baseline techniques in terms of model evaluation criteria for text sentiment classification.<\/jats:p>","DOI":"10.1155\/2023\/1896556","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:20:06Z","timestamp":1703722806000},"page":"1-14","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Sentiment Classification Method with the Help of Neighbors and a Hybrid of RNN Models"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"http:\/\/orcid.org\/0009-0008-3563-2266","authenticated-orcid":true,"given":"Israa K.","family":"Salman Al-Tameemi","sequence":"first","affiliation":[{"name":"Computerized Intelligence Systems Laboratory, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51368, Iran"},{"name":"State Company for Engineering Rehabilitation and Testing, Iraqi Ministry of Industry and Minerals, Baghdad 10011, Iraq"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8548-976X","authenticated-orcid":true,"given":"Mohammad-Reza","family":"Feizi-Derakhshi","sequence":"additional","affiliation":[{"name":"Computerized Intelligence Systems Laboratory, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51368, Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8949-9180","authenticated-orcid":true,"given":"Saeed","family":"Pashazadeh","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51368, Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1672-149X","authenticated-orcid":true,"given":"Mohammad","family":"Asadpour","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51368, Iran"}]}],"member":"98","reference":[{"key":"1","article-title":"A comprehensive review of visual-textual sentiment analysis from social media networks","author":"I. 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