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- W4285291935 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." @default.
- W4285291935 created "2022-07-14" @default.
- W4285291935 creator A5033244749 @default.
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- W4285291935 date "2022-05-20" @default.
- W4285291935 modified "2023-10-10" @default.
- W4285291935 title "Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models" @default.
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- W4285291935 doi "https://doi.org/10.4018/ijssci.300361" @default.
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