Abstract
With the rapid expansion of the Internet and social media, there has been an explosion of text data, containing valuable emotional insights. Sentiment analysis, aimed at discerning and processing people's emotional inclinations, opinions, and perspectives, has emerged as a pivotal area within natural language processing (NLP). This research explores the application of Deep Learning-based BERT models, particularly DistilBERT, in sentiment analysis. It highlights the importance of sentiment analysis in understanding emotional insights from vast text data and introduces BERT models as revolutionary methodologies for enhancing accuracy and efficiency. The study conducts experiments using the SST2 dataset, showcasing the effectiveness of DistilBERT in sentiment classification tasks. Overall, it underscores the transformative potential of BERT models in revolutionizing sentiment analysis methodologies and driving advancements in natural language processing research.
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