Sentiment Analysis and Knowledge Extraction from Social Media Using BERT-Based Models: A Transformer Approach to Understanding Public Opinion

Abstract

The proliferation of social media platforms has led to an unprecedented volume of user-generated content expressing personal opinions, emotions, and attitudes. Extracting meaningful insights from this massive stream of data presents a significant opportunity for organizations, governments, and researchers. This paper explores the application of Bidirectional Encoder Representations from Transformers (BERT) for sentiment analysis and knowledge extraction from social media text. The study highlights how BERT based models outperform traditional sentiment analysis techniques by capturing contextual information more effectively. We demonstrate how transformer-based models are used to extract trends, detect emerging topics, and evaluate public sentiment with high accuracy. Furthermore, the paper discusses applications of this technology in areas such as policy-making, brand monitoring, crisis response, and customer feedback analysis. Through real world use cases and experimental findings, we emphasize the model’s scalability and accuracy, offering valuable insights for stakeholders in data driven decision-making.

Keywords: Sentiment analysis, BERT, NLP, social media mining, knowledge extraction