Nikoo Karimi - Engineering Science Department,College of EngineeringUniversity of TehranTehran, Iran
Ehsan Maani Miandoab - Engineering Science Department,College of EngineeringUniversity of TehranTehran, Iran
Ali Fahim - Engineering Science Department,College of EngineeringUniversity of TehranTehran, Iran

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.

sentiment analysis, BERT, GPT-۲, cryptocurrency