Abstract:
Market sentiment, as driven by news and online discussion sites is increasingly impacting algorithmic
trading systems. In this work, we analyze financial news headlines from Yahoo Finance,
enhanced with related discussion context from Reddit, to identify market sentiment for Bitcoin.
We trained various machine learning and deep learning sentiment classifiers with a dataset of
11,293 Bitcoin-related text samples primarily extracted from Yahoo Finance news headlines
and aligned with relevant Reddit discussion content. The dataset had three classes of sentiment
which were Positive, Neutral and Negative. The extracted data was fetched from news headlines
ranging for five years. Multiple classification models were testes, the classical machine learning
models, deep learning architectures and then transformer based language models, which enabled
a systematic comparison across different models. In baseline models, Logistic Regression
achieved the best performance (accuracy 71.7%, F1=0.704), and among neural network-based
ones, BiLSTM does so with accuracy of 70.5%. Transformer-based models were subsequently
fine-tuned and evaluated for comparative performance. The model FinBERT achieved improved
classification performance (accuracy of 83.7%, F1=0.835), which was further confirmed statically
by McNemar’s statistical test as compared with the other models. FinBERT showed the
effectiveness of domain specific contextual pretraining for the financial sentiment because it
was able to outperform all the other models by a large margin. This study highlights the importance
of incorporating sentiment aware modeling into the financial prediction workflows and
also provide insights that would be needed for future development of reliable trading strategies
