Hybrid deep learning for sentiment analysis of online student experiences - Raja Ouadad; Hicham Mouncif (Institute of Advanced Engineering and Science, 2026)
The COVID-19 pandemic disrupted millions of lives worldwide, and social media platforms became a significant outlet for people to share their emotions and experiences, providing valuable insights into the challenges and opportunities of remote education. This paper analyzes student sentiments about online learning during the pandemic using Twitter data. An experimental approach is developed to analyze public comments, focusing on the sentiment expressed in tweets related to online education. A hybrid deep learning model, based on the logistic regression (LR) sentiment model, is used to predict sentiment from a large dataset of online learning-related tweets. After performing n-gram analysis to extract key topics, tweets are classified into sentiment classes. The proposed convolutional long short term memory (Conv-LSTM) and convolutional bidirectional long short-term memory (Conv-BiLSTM) models are trained on tweets annotated with granular sentiment classifications, achieving validation accuracies of 93% and 95%, respectively. This work provides meaningful insights into the emotional effects of online learning during the pandemic, contributing to the understanding of students' experiences and challenges in remote education.