Sentiment Analysis in Social Media: Leveraging BERT for Enhanced Accuracy

Authors

  • Jiawei Wu Illinois Institute of Technology
  • Ping Qu Maharishi International University
  • Beibei Zhang Xi'an Jiaotong University
  • Zhanxin Zhou Northern Arizona University

DOI:

https://doi.org/10.5281/zenodo.13144389

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n4a20

Keywords:

Sentiment Analysis, Social Media, BERT, Transformer Models, Natural Language Processing, Contextual Embeddings

Abstract

The rapid growth of social media has generated vast amounts of user-generated content, making sentiment analysis a crucial tool for understanding public opinion. This paper explores the application of Bidirectional Encoder Representations from Transformers (BERT) in sentiment analysis of social media texts. By leveraging BERT's contextual embeddings, we aim to enhance the accuracy of sentiment classification. Our study compares BERT with traditional machine learning models and other deep learning approaches, demonstrating BERT's superiority in capturing the nuances of social media language. Additionally, we investigate the challenges and limitations of using BERT in this context, such as handling sarcasm, slang, and the dynamic nature of social media content. Our results indicate a significant improvement in sentiment analysis performance, highlighting the potential of BERT for practical applications in monitoring and analyzing public sentiment on social media platforms.

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Author Biographies

Jiawei Wu, Illinois Institute of Technology

Engineering in Artificial Intelligence for Computer Vision and Control, Illinois Institute of Technology, Chicago, IL, USA.

Ping Qu, Maharishi International University

Computer Science, Maharishi International University, Fairfield, IA, USA.

Beibei Zhang, Xi'an Jiaotong University

Software Engineering, Xi'an Jiaotong University, Xi'an, China.

Zhanxin Zhou, Northern Arizona University

Affiliation: Northern Arizona University.

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Published

2024-08-01

How to Cite

[1]
J. Wu, P. Qu, B. Zhang, and Z. Zhou, “Sentiment Analysis in Social Media: Leveraging BERT for Enhanced Accuracy”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 4, pp. 143–149, Aug. 2024.

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