Deep sentiment analysis with data augmentation in distance education during the pandemic

During the global Covid-19 pandemic, the shutdown of educational institutes has resulted in a phenomenal surge in online learning. Academic activities were shifted to online learning platforms to restrict the influence of COVID-19 and block its spread. For both students and parents, the efficiency of online learning is a major concern, particularly in terms of its suitability for students and teachers, as well as its technological applicability in various social situations. Before the online learning approach can be employed on such a big scale, such challenges must be viewed from different aspects. This study aims to assess the efficiency of online learning by examining individuals' sentiments toward it. Due to social media becoming such an essential form of communication, people's opinions can be observed on platforms like Twitter. The main motivation is to use a Twitter dataset featuring online learning-related tweets. Briefly, we focused on specifying the impact of the Covid-19 pandemic on education in many aspects and parameters by using tweets. We utilized natural language processing models for text classification with a gathered dataset that includes fetching tweets consisting of Covid-19 and education topics. We developed a fine-tuned Long short-term memory (LSTM) model that utilizes data augmentation for classifying the emotional states of individuals. With the deep sentiment analysis model that we proposed, we observed that the negative sentiments were experienced more.