RT info:eu-repo/semantics/article
T1 Sentence-level classification using parallel fuzzy deep learning classifier
A1 Es-Sabery, Fatima
A1 Hair, Abdellatif
A1 Qadir, Junaid
A1 Sainz de Abajo, Beatriz
A1 García Zapirain, Begoña
A1 Torre Díez, Isabel de la
K1 Deep learning
K1 Convolutional neural network
K1 Sentiment analysis
AB At 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.
PB IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC.
SN 2169-3536
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/67945
UL https://uvadoc.uva.es/handle/10324/67945
LA eng
NO IEEE Access, Enero 2021, vol. 9, p. 17943-17985.
NO Producción Científica
DS UVaDOC
RD 25-nov-2024