Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets

Naglaa Abdelhady, Taysir Hassan A. Soliman, Mohammed F. Farghally
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Abstract

Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people’s lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people’s feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model. We also compare the proposed method to various deep learning models: CNN, LSTM, and BiLSTM. Experiments are performed on three different Arabic datasets related to COVID-19 and vaccines. Empirical findings show that the proposed model outperformed the other models’ results by achieving F-measures of 76.76%, 87.%, and 80.5% on the SenWave, AraCOVID19-SSD, and ArCovidVac datasets, respectively.
堆叠-CNN-BiLSTM-COVID:用于阿拉伯语 COVID-19 推文情感分析的有效堆叠集合深度学习框架
社交网络在广告、思想分享和舆论形成方面很受欢迎。由于 COVID-19,在社交媒体上传播的冠状病毒信息直接影响着人们的生活。个人有时能很好地应对,但往往会妨碍日常活动。因此,分析人们的感受非常重要。情感分析可以从文本中识别观点或情感。在本文中,我们提出了一个有效的模型,利用卷积神经网络(CNN)和双向长短期记忆(BiLSTM)的优势,使用堆叠集合学习模型对阿拉伯语推文进行分类。首先,使用单词嵌入模型将推文表示为向量,然后用 CNN 提取文本特征,最后用 BiLSTM 获取文本的上下文信息。我们分别使用了 Aravec、FastText 和 ArWordVec 来评估单词嵌入对我们模型的影响。我们还将提议的方法与各种深度学习模型进行了比较:CNN、LSTM 和 BiLSTM。我们在与 COVID-19 和疫苗相关的三个不同的阿拉伯语数据集上进行了实验。实证结果表明,所提出的模型在 SenWave、AraCOVID19-SSD 和 ArCovidVac 数据集上的 F-measures 分别达到了 76.76%、87.% 和 80.5%,优于其他模型的结果。
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