Enhancing Sentiment Understanding In Social Media Content With Bert Algorithm

Abstract

Sentiment analysis, also known as opinion mining, is a crucial task in natural language processing (NLP), with widespread applications in understanding public opinion, market research, and social media monitoring. This research paper investigates the use of the BERT (Bidirectional Encoder Representations from Transformers) algorithm to enhance sentiment understanding in social media content. We delve into the capabilities of BERT, its application in sentiment analysis, and its advantages over traditional techniques. Through empirical experiments using real-world social media data, we demonstrate the efficacy of BERT in improving sentiment analysis accuracy. This paper contributes to advancing sentiment analysis methodologies, particularly in the context of social media, and discusses potential future directions in this evolving field.