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

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

Sabri Sabri Mohammed, Brahami Menaouer, Abid Faten Fatima Zohra, Matta Nada
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 20
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.300361
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MLA

Mohammed, Sabri Sabri, et al. "Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models." IJSSCI vol.14, no.1 2022: pp.1-20. http://doi.org/10.4018/IJSSCI.300361

APA

Mohammed, S. S., Menaouer, B., Faten Fatima Zohra, A., & Nada, M. (2022). Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-20. http://doi.org/10.4018/IJSSCI.300361

Chicago

Mohammed, Sabri Sabri, et al. "Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-20. http://doi.org/10.4018/IJSSCI.300361

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Abstract

In today’s digital era, Twitter’s data has been the focus point among researchers as it provides specific data and 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. Our experimental results indicate that fuzzy deep learning achieves the best accuracy (i.e. 0.916) with word embeddings.

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