Sentiment Analysis in Social Media: Leveraging BERT for Enhanced Accuracy
DOI:
https://doi.org/10.5281/zenodo.13144389ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a20Keywords:
Sentiment Analysis, Social Media, BERT, Transformer Models, Natural Language Processing, Contextual EmbeddingsAbstract
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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