Combining BERT and CNN for Sentiment Analysis A Case Study on COVID-19

This research focuses on sentiment analysis to understand public opinion on various topics, with an emphasis on COVID-19 discussions on Twitter. By utilizing state-of-the-art Machine Learning (ML) and Natural Language Processing (NLP) techniques, the study analyzes sentiment data to provide valuable insights. The process begins with data preparation, involving text cleaning and length filtering to optimize the dataset for analysis. Two models are employed: a Bidirectional Encoder Representations from Transformers (BERT)-based Deep Learning (DL) model and a Convolutional Neural Network (CNN). The BERT model leverages transfer learning, demonstrating strong performance in sentiment classification, while the CNN model excels at extracting contextual features from the input text. To further enhance accuracy, an ensemble model integrates predictions from both approaches. The study emphasizes the ensemble technique’s value for more precise sentiment analysis. Evaluation metrics, including accuracy, classification reports, and confusion matrices, validate the effectiveness of the proposed models and the ensemble approach. This research contributes to the growing field of social media sentiment analysis, particularly during global health crises like COVID-19, and underscores its potential to aid informed decision-making based on public sentiment.

@article{Kumar2024,
  title     = {Combining BERT and CNN for Sentiment Analysis A Case Study on COVID-19},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Gunjan Kumar and Renuka Agrawal and Kanhaiya Sharma and Pravin Ramesh Gundalwar and Aqsa kazi and Pratyush Agrawal and Manjusha Tomar and Shailaja Salagrama},
  doi       = {10.14569/IJACSA.2024.0151069},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151069}
}