Sentiment Analysis Predictions in Digital Media Content using NLP Techniques

In the current digital landscape, understanding sentiment in digital media is crucial for informed decision-making and content quality. The primary objective is to improve decision-making processes and enhance content quality within this dynamic environment. To achieve this, a comprehensive comparative analysis of NLP for tweet sentiment analysis was conducted, revealing compelling insights. The BERT pre-trained model stood out, achieving an accuracy rate of 94.56%, emphasizing the effectiveness of transfer learning in text classification. Among machine learning algorithms, the Random Forest model excelled with an accuracy rate of 70.82%, while the K Nearest Neighbours model trailed at 55.36%. Additionally, the LSTM model demonstrated excellence in Recall, Precision, and F1 metrics, recording values of 81.12%, 82.32%, and 80.12%, respectively. Future research directions include optimizing model architecture, exploring alternative deep learning approaches, and expanding datasets for improved generalizability. While valuable insights are provided by our study, it is important to acknowledge its limitations, including a Twitter-centric focus, constrained model comparisons, and binary sentiment analysis. These constraints highlight opportunities for more nuanced and diverse sentiment analysis within the digital media landscape.

@article{Radaideh2023,
  title     = {Sentiment Analysis Predictions in Digital Media Content using NLP Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Abdulrahman Radaideh and Fikri Dweiri},
  doi       = {10.14569/IJACSA.2023.0141128},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141128}
}