Effect of Word Embedding in Online Learning Tweet Multi- Classification with CNN and LSTM Variants

Abstract

The paper examines the effectiveness of multi-class sentiment analysis strategies using deep learning methods for imbalanced and balanced datasets with and without word embeddings. Seven models, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, LSTM-CNN, Bidirectional LSTM (BILSTM), CNN-BILSTM and Two layers CNN are compared. A dataset consisting of 23,168 tweets was gathered from online learning platforms between 2020 and 2021. The performance of sentiment categorization was evaluated based on accuracy, precision, recall, and F1-score. The study presents three main findings: (1) a comparison of the effectiveness of seven sentiment analysis algorithms, (2) the clear advantage of pre-trained Word2vec, and (3) the capability to achieve a balanced sentiment categorization using Twitter data. The LSTM-CNN model utilizing Word2vec word embedding outperformed several models, achieving an accuracy of 89.66% and a precision, recall, and F1-Score of 90.00% for the testing results. The experimental results confirmed that this methodology enhanced the accuracy of sentiment classification compared to standard methods and exhibited superior classification performance. The empirical research showed that the LSTM-CNN method was fast, efficient, and viable, making it a potentially better option for optimizing online learning rules. The study provides valuable insights to analytics professionals and academicians engaged in text analysis. It focuses on the performance evaluation of essential algorithms in sentiment classification, particularly emphasizing the data balancing technique in deep learning hybrid models.