2026-05-05T09:08:49Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/750962025-02-26T12:18:26Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Es-Sabery, Fatima Es-Sabery, Ibrahim Qadir, Junaid Sainz de Abajo, Beatriz García Zapirain, Begoña 2025-02-21T07:00:56Z 2025-02-21T07:00:56Z 2024 Journal of Big Data, Diciembre 2024, vol. 11, n. 1, artículo n. 176, p. 1-55. 2196-1115 https://uvadoc.uva.es/handle/10324/75096 10.1186/s40537-024-01014-4 1 1 55 Journal of Big Data 11 2196-1115 Producción Científica In recent years, research on opinion mining from X (formerly Twitter) has rapidly advanced, focusing on processing tweets to determine user sentiments about events. Many researchers prefer using machine and deep learning techniques for this analysis. This work proposes a novel approach integrating the C4.5 procedure, fuzzy rule patterns, and convolutional neural networks. The approach involves six steps: pre-processing to remove noisy data, vectorizing tweets with word embedding, extracting sentiment and contextual features using convolutional neural networks, fuzzifying outputs with a Gaussian fuzzifier to handle ambiguity, constructing a fuzzy tree and rule base using a fuzzy version of C4.5, and classifying tweets with fuzzy General Reasoning. This method combines the benefits of convolutional neural networks and C4.5 while addressing imprecise data with fuzzy logic. Implemented on a Hadoop framework-based cluster with five computing units, the approach was extensively tested. The results showed that the model performs exceptionally well on the COVID-19_Sentiments dataset, surpassing other classification algorithms with a precision rate of 94.56%, false-negative rate of 5.28%, classification rate of 95.15%, F1-score of 94.63%, kappa statistic of 95.12%, execution time of 11.81 s, false-positive rate of 4.26%, error rate of 4.26%, specificity of 95.74%, recall of 94.72%, stability with a mean deviation standard of 0.09%, convergence starting around the 75th round, and significantly reduced complexity in terms of time and space. Este trabajo ha sido financiado por el grupo de investigación eVida, de la Universidad de Deusto, como parte del proyecto de investigación: Grant IT 905-16. application/pdf eng Springer Nature info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ "© Todos los derechos reservados". Propietario de los derechos: Springer Nature Atribución-NoComercial-CompartirIgual 4.0 Internacional Sentiment analysis Opinion mining Big data Fuzzy version of C4.5 procedure Convolutional neural network Fuzzy rule pattern Hadoop framework opinion mining Sentiment analysis A hybrid Hadoop-based sentiment analysis classifier for tweets associated with COVID-19 utilizing two machine learning algorithms: CNN, and fuzzy C4.5 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://journalofbigdata.springeropen.com/articles/10.1186/s40537-024-01014-4 SI