Sentiment Analysis of Cryptocurrency Data: BERT vs. GPT-۲ - A Comparative Study

This paper compares the two language models, BERT and GPT-۲, in order to investigate sentiment in the context of cryptocurrency text data. A dataset of cryptocurrency-related text from various sources is labeled using a zero-shot classifier, and the BERT and GPT-۲ models are fine-tuned on this dataset. The study evaluates the performance of BERT and GPT-۲ in sentiment classification tasks, considering metrics such as recall, accuracy, and F۱-score. Results reveal that BERT performs more effectively than GPT-۲ in the area of understanding and classifying sentiment, specifically negative sentiment. The study emphasizes the significance of model goals and design for achieving superior performance in natural language processing tasks.

کلیدواژه ها:

sentiment analysis ، BERT ، GPT-۲ ، cryptocurrency