Sentence-level classification using parallel Fuzzy Deep Learning Classifier

Vista sencilla de ítem
dc.contributor.authorEs-sabery, Fatima
dc.contributor.authorHair, Abdellatif
dc.contributor.authorQadir, Junaid
dc.contributor.authorSainz de Abajo, Beatriz
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorTorre Díez, Isabel de la
dc.date.accessioned2025-06-10T17:15:12Z
dc.date.available2025-06-10T17:15:12Z
dc.date.issued2021-02-01
dc.date.updated2025-06-10T17:15:12Z
dc.description.abstractAt present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.en
dc.description.sponsorshipThis work was supported by the eVida Research Group, University of Deusto, Bilbao, Spain, under Grant IT 905-16en
dc.identifier.citationEs-Sabery, F., Hair, A., Qadir, J., Sainz-De-Abajo, B., Garcia-Zapirain, B., & Torre-DIez, I. (2021). Sentence-level classification using parallel Fuzzy Deep Learning Classifier. IEEE Access, 9, 17943-17985. https://doi.org/10.1109/ACCESS.2021.3053917
dc.identifier.doi10.1109/ACCESS.2021.3053917
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/2998
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherDeep learning
dc.subject.otherConvolutional neural network (CNN)
dc.subject.otherSentiment analysis
dc.subject.otherFeedforward neural network (FFNN)
dc.subject.otherFuzzy logic
dc.subject.otherHadoop framework
dc.subject.otherMapReduce
dc.subject.otherHadoop Distributed File System (HDFS).
dc.titleSentence-level classification using parallel Fuzzy Deep Learning Classifieren
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage17985
oaire.citation.startPage17943
oaire.citation.titleIEEE Access
oaire.citation.volume9
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
sabery_sentence_2021.pdf
Tamaño:
3.83 MB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos