Deep Learning-Based Sentiment Analysis on Education During the COVID-19 Pandemic

Abstract

The global COVID-19 pandemic in 2020 has led to catastrophic economic and social disruption. The pandemic has affected almost every aspect of our lives, including health, food, business organizations, and education. An essential shift in the higher education field has been occurred with the digitalization of instruction. In attempt to combat the pandemic, several higher education institutions throughout the world have begun to offer undergraduate and graduate courses online, either asynchronously or synchronously. During this period, people make considerable use of social media to gain news, information, social connections, and support. As a result, the immense quantity of electronic text documents has been shared on the Web related to COVID-19. In this paper, we present a deep learning-based sentiment analysis approach to analyze the impact of COVID-19 pandemic on the higher education. In this regard, the predictive performance of conventional machine learning algorithms (support vector machines, naïve bayes, logistic regression, and random forest) and deep neural networks (convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit) are compared to each other. In addition, the empirical results obtained by the bidirectional encoder representations from transformers (BERT) have been evaluated. The comprehensive empirical results with different text representation models and classification algorithms indicate that deep neural networks can yield promising results for the task of analyzing the impact of COVID-19 related text documents on the higher education.

Keywords

• Deep Learning
• Sentiment Analysis
• Text Mining
• COVID-19
• Higher Education

COVID-19 Pandemi Döneminde Eğitimde Derin Öğrenmeye Dayalı Duygu Analizi

Abstract

Keywords: Deep Learning, Sentiment Analysis, Text Mining, COVID-19, Higher Education Öz 2020 yılında küresel COVID-19 pandemisi, ciddi ekonomik ve toplumsal kesintilere yol açtı. Pandemi sağlık, gıda, iş organizasyonları ve eğitim dahil olmak üzere hayatımızın neredeyse her alanını etkiledi. Eğitimin dijitalleştirilmesi ile birlikte yükseköğretim alanında önemli bir değişiklik yaşanmıştır. Pandemi ile mücadele amacıyla, dünya çapında birçok yükseköğretim kurumu, eş zamanlı veya eş zamansız olarak lisans ve lisansüstü derslerini çevrimiçi olarak sunmaya başlamıştır. Bu süre zarfında insanlar haber, bilgi, destek almak için ve sosyal bağlantılar kurmak için sosyal medyadan ciddi ölçüde yararlanmaktadırlar. Bu sayede, COVID-19 ile ilgili olarak Web'de çok miktarda elektronik metin belgesi paylaşılmıştır. Bu makalede, COVID-19 salgınının yüksek öğrenim üzerindeki etkisini analiz etmek için derin öğrenime dayalı bir duygu analizi yaklaşımı sunuyoruz. Bu bağlamda, geleneksel makine öğrenimi algoritmalarının (vektör destek makineleri, naive bayes, lojistik regresyon ve rastgele orman) ve derin sinir ağlarının (evrişimli sinir ağı, tekrarlı sinir ağı, uzun süreli bellek ve gated tekrarlı birim) performansları karşılaştırılmıştır. Buna ek olarak, transformerlardan gelen çift yönlü enkoder gösterimleri (BERT) tarafından elde edilen ampirik sonuçlar da değerlendirilmiştir. Farklı metin gösterim modelleri ve sınıflandırma algoritmalarına sahip kapsamlı ampirik sonuçlar, derin sinir ağlarının COVID-19 ile ilgili metin belgelerinin yüksek eğitim üzerindeki etkisini analiz etme görevi için umut verici sonuçlar verebileceğini göstermektedir.

Keywords

• Derin Öğrenme
• Duygu Analizi
• Metin Madenciliği
• COVID-19
• Yüksek Öğrenim

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