Monitoring Dynamics of Emotional Sentiment in Social Network Commentaries

Authors: Ismail Hossain, Sai Puppala, Md Jahangir Alam, Sajedul Talukder

Paper

Abstract

The proliferation of social media offers a real-time reflection of public sentiments. Sentiment analysis on such platforms yields crucial insights for sectors like market research, politics, business strategy, and public health. In this study, we introduce an innovative framework to examine evolving sentiments in social media comments and understand their wider implications. Utilizing a pre-trained BERT base uncase model, we estimate emotional values from comments and align them with various sentiment trends such as Approval, Toxicity, and Neutral, among others. By leveraging machine learning, we train on a distinctive dataset, correlating emotional values with sentiment trends to generate trend likelihood scores. Through a bottom-up methodology, we compile emotional ratings across comment threads to forecast overarching sentiment scores. Our results reveal that the BERT base uncase model excels in emotional prediction, achieving an AUC of 0.91. Meanwhile, Decision Tree models stand out, registering an F1 score above 0.40 on a macro average basis.

Key Contributions

• Placeholder — to be added.

Resources

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BibTeX

@inproceedings{hossain2023monitoring,
  title     = {Monitoring Dynamics of Emotional Sentiment in Social Network Commentaries},
  author    = {Hossain, Ismail and Puppala, Sai and Alam, Md Jahangir and Talukder, Sajedul},
  booktitle = {ASONAM 2023},
  year      = {2023},
  url       = {https://doi.org/10.1145/3625007.3627730}
}