Bitcoin price prediction using transfer learning on financial micro-blogs

We present a methodology for predicting the price of Bitcoin using Twitter data and historical Bitcoin prices. Bitcoin is the largest cryptocurrency that, in terms of market capitalization, represents over 110 billion dollars. The news volume is rapidly growing, and Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its popularity. Using modern Natural Language Processing models for transfer learning, we analyze tweets’ meaning and calculate sentiment using the NLP transformers. We combine the daily historical Bitcoin price data with the daily sentiment and predict the next day’s price using auto-regressive models for time-series forecasting. The results show that modern approaches for sentiment analysis, time-series forecasting, and transfer-learning are applicable for predicting Bitcoin price when we include sentiment extracted from financial micro-blogs as input. The results show improvement when compared to the old approaches using only historical price data. Additionally, we show that the NLP models based on transfer-learning methodologies improve the efficiency in sentiment extraction in financial micro-blogs compared to standard sentiment extraction methods.Published versio

Comparing the performance of ChatGPT and state-of-the-art climate NLP models on climate-related text classification tasks

Recently, there has been a surge in general-purpose language models, with ChatGPT being the most advanced model to date. These models are primarily used for generating text in response to user prompts on various topics. It needs to be validated how accurate and relevant the generated text from ChatGPT is on the specific topics, as it is designed for general conversation and not for context-specific purposes. This study explores how ChatGPT, as a general-purpose model, performs in the context of a real-world challenge such as climate change compared to ClimateBert, a state-of-the-art language model specifically trained on climate-related data from various sources, including texts, news, and papers. ClimateBert is fine-tuned on five different NLP classification tasks, making it a valuable benchmark for comparison with the ChatGPT on various NLP tasks. The main results show that for climate-specific NLP tasks, ClimateBert outperforms ChatGPT