Topic Modeling and Sentiment Analysis of Online Education in the COVID-19 Era Using Social Networks Based Datasets

• DOI: 10.3390/electronics11050715
• Publication Date: 2022-02-25T15:00:40Z
• Authors: Samer Abdulateef ...; Naseer Ahmed Khan; Xuequn Shang
• Keywords: Misinformation; Sentiment Analysis; Disinformation; Autoencoder

Sentiment Analysis (SA) is a technique to study people’s attitudes related textual data generated from sources like Twitter. This suggested powerful and effective that can tackle the large contents specifically examine attitudes, sentiments, fake news of “E-learning”, which considered big challenge, as online education sector great importance. On other hand, misinformation COVID-19 have confused parents, students, teachers. An efficient detection approach should be used gather more precise information in order identify disinformation. Tweet records (people’s opinions) gained significant attention worldwide for understanding behaviors attitudes. SA still does not provide clear picture available these tweets, especially if this affect field E-learning. has proposed denoising AutoEncoder eliminate noise information, attentional mechanism fusion features parts where multi-level ELM-AE with LSTM applied task classification. Experiments show our obtains higher F1-score value 0.945, compared different state-of-the-art approaches, various sizes testing training datasets. Based on knowledge, model learn unified set obtain good performance, better results than one learned subset features.