  In this paper, we explore the usability of different natural language
processing models for the sentiment analysis of social media applied to
financial market prediction, using the cryptocurrency domain as a reference. We
study how the different sentiment metrics are correlated with the price
movements of Bitcoin. For this purpose, we explore different methods to
calculate the sentiment metrics from a text finding most of them not very
accurate for this prediction task. We find that one of the models outperforms
more than 20 other public ones and makes it possible to fine-tune it
efficiently given its interpretable nature. Thus we confirm that interpretable
artificial intelligence and natural language processing methods might be more
valuable practically than non-explainable and non-interpretable ones. In the
end, we analyse potential causal connections between the different sentiment
metrics and the price movements.
