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.
Funding
This work was supported by the eVida Research Group, University of Deusto, Bilbao, Spain, under Grant IT 905-16.