Deep Neural Network for Arabic Tweets Sentiment Analysis Related to COVD-19
Keywords:
Arabic Text, Deep Learning, Machine Learning, Opinion mining, NLP, Covid 19, Coronavirus, Naïve Bayes, Sentiment Analysis;, LSTM, CNNAbstract
Aside from the Coronavirus pandemic, several other major crises erupted worldwide. Various industries have been irreparably harmed, and many organizations have succumbed to the calamity. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. The study examines the emotional orientations of Arabic persons who use social media, specifically the Twitter platform. From November 2020 to January 2021, we gathered data from Twitter. Tweets have been sent from a number of Arab cities. Natural Language Processing (NLP) and Machine Learning (ML) methods are used to identify whether an opinion's sentiment is favorable, negative, or neutral. This study gathers Arabic tweets and then manually annotates them to classify them as negative, positive, neutral, etc. In this work, word embedding and TFIDF are employed as vector features, with Long Short-Term Memory (LSTM) and Naive Bayes used for classification. This paper offers a learned LSTM model and a Naive Bayes model based on the collected tweets, leveraging two powerful ML methods. The LSTM model is more effective than the Nave Bayes model since its superior performance. This is noted by achieving an accuracy of 99% for the LSTM model. The analysis of this study aids various governments and corporate entities in better understanding public attitude and behavior in the face of the pandemic and making strategic decisions in response. Furthermore, this study focuses on data visualization by exhibiting an emotion plot and a word cloud. This study is an extended version of our work "the Sentiment Analysis of Arabic Tweets Related to COVID-19 Using Deep Neural Network".
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