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.

本研究探讨了不同的自然语言处理模型在社交媒体情感分析中的可用性，应用于金融市场预测，以加密货币领域为参考。我们研究了不同情感度量与比特币价格波动之间的相关性。通过实验我们发现有一个模型优于其他 20 多个公开模型，且通过 fine-tune 可以得到更好的性能，其解释性更强。因此我们证实可解释的人工智能和自然语言处理方法可能比不可解释的方法更有实际价值。最后，我们分析了不同情感度量与价格波动之间的潜在因果关系。