@article{uneatlantico17792,
           month = {Abril},
          number = {1},
         journal = {Scientific Reports},
          author = {Komal Azim and Alishba Tahir and Mobeen Shahroz and Hanen Karamti and Annia A. V{\'a}zquez and Angel Olider Rojas Vistorte and Imran Ashraf},
           title = {Ensemble stacked model for enhanced identification of sentiments from IMDB reviews},
          volume = {15},
            year = {2025},
             url = {http://repositorio.uneatlantico.es/id/eprint/17792/},
        abstract = {The emergence of social media platforms led to the sharing of ideas, thoughts, events, and reviews. The shared views and comments contain people?s sentiments and analysis of these sentiments has emerged as one of the most popular fields of study. Sentiment analysis in the Urdu language is an important research problem similar to other languages, however, it is not investigated very well. On social media platforms like X (Twitter), billions of native Urdu speakers use the Urdu script which makes sentiment analysis in the Urdu language important. In this regard, an ensemble model RRLS is proposed that stacks random forest, recurrent neural network, logistic regression (LR), and support vector machine (SVM). The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. The Urdu hack library was used to preprocess the Urdu data, which includes preprocessing operations including normalizing individual letters, merging them, including spaces, etc. concerning punctuation. The problem of accurately encoding Urdu characters and replacing Arabic letters with their Urdu equivalents is fixed by the normalization module. Several models are adopted in this study for extensive evaluation of their accuracy for Urdu sentiment analysis. While the results promising, among machine learning models, the SVM and LR attained an accuracy of 87\%, according to performance criteria such as F-measure, accuracy, recall, and precision. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) was 84\%. The suggested ensemble RRLS model performs better than other learning algorithms and achieves a 90\% accuracy rate, outperforming current methods. The use of the synthetic minority oversampling technique (SMOTE) is observed to improve the performance and lead to 92.77\% accuracy.},
        keywords = {Sentiment analysis, Text classification, Urdu text analysis, Machine learning, Ensemble learning}
}