Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models

In today's digital era, Twitter's data has been the focus point among researchers as it provides specific data in a wide variety of fields. Furthermore, Twitter's daily usage has surged throughout the coronavirus disease (COVID-19) period, presenting a unique opportunity to analyze the content and sentiment of COVID-19 tweets. In this paper, a new approach is proposed for the automatic sentiment classification of COVID-19 tweets using the adaptive neuro-fuzzy inference system (ANFIS) models. The entire process includes data collection, pre-processing, word embedding, sentiment analysis, and classification. Many experiments were accomplished to prove the validity and efficiency of the approach using datasets COVID-19 tweets, and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes. The experimental results indicate that fuzzy deep learning achieves the best accuracy (i.e., 0.916) with word embeddings.

Automated summary

This paper proposes a new approach for the automatic sentiment classification of COVID-19 tweets using ANFIS models and demonstrates its effectiveness and efficiency through experiments.