Sentiment Analysis of Covid19 Tweets Using A MapReduce Fuzzified Hybrid Classifier Based On C4.5 Decision Tree and Convolutional Neural Network

This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applied several pre-processing techniques. Secondly, used fastText vectoring analysed tweets. Thirdly, implemented CNN extracting selecting pertinent features from Fourthly, fuzzified output using Gaussian Fuzzification (GF) coping with vague data. Then fuzziness creating rules. Finally, General Fuzziness Reasoning (GFR) approach classifying In summary, our integrates advantages techniques overcomes shortcomings ambiguous data in tweets FRBS, is three-phase: fuzzification phase GF, inference mechanism C4.5, defuzzification GFR. Also, to give ability deal massive data, it on Hadoop framework five computers. The experiential findings confirmed that operates excellently compared other chosen models form literature.