基于自适应神经模糊推理系统模型的COVID-19推文情感分析

Sabri Mohammed, Menaouer Brahami, Abid Faten Fatima Zohra, M. Nada
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引用次数: 6

摘要

在当今的数字时代,Twitter的数据一直是研究人员关注的焦点,因为它提供了广泛领域的具体数据。此外,在新冠肺炎疫情期间,推特的日使用量大幅增加,这为分析新冠肺炎推文的内容和情绪提供了独特的机会。本文提出了一种基于自适应神经模糊推理系统(ANFIS)模型的Covid-19推文情绪自动分类新方法。整个过程包括数据收集、预处理、词嵌入、情感分析和分类。利用covid - tweet数据集进行了大量实验,证明了该方法的有效性和效率,并完成了数据约简过程,在保留重要数据集属性的情况下实现了相当大的尺寸缩减。我们的实验结果表明,模糊深度学习在词嵌入方面达到了最好的准确率(0.916)。
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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 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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