During the COVID-19 pandemic, many research studies have been conducted to examine the impact of the outbreak on the financial sector, especially on cryptocurrencies. Social media, such as Twitter, plays a significant role as a meaningful indicator in forecasting the Bitcoin (BTC) prices. However, there is a research gap in determining the optimal preprocessing strategy in BTC tweets to develop an accurate machine learning prediction model for bitcoin prices. This paper develops different text preprocessing strategies for correlating the sentiment scores of Twitter text with Bitcoin prices during the COVID-19 pandemic. We explore the effect of different preprocessing functions, features, and time lengths of data on the correlation results. Out of 13 strategies, we discover that splitting sentences, removing Twitter-specific tags, or their combination generally improve the correlation of sentiment scores and volume polarity scores with Bitcoin prices. The prices only correlate well with sentiment scores over shorter timespans. Selecting the optimum preprocessing strategy would prompt machine learning prediction models to achieve better accuracy as compared to the actual prices.

The coronavirus disease 2019 (COVID-19) outbreak has created a great challenge for many industries, including healthcare systems, E-commerce, and transportation. Cryptocurrencies, such as Bitcoin (BTC), is an alternative class of digital assets primarily used as a medium of exchange. In the financial market, data collected from social media such as tweets, posts, and can assist in an early indication of market sentiment. This has frequently been conducted on Twitter data. In particular, this paper provides a corpus of tweet text for Bitcoin-related tweets during the summer of the COVID-19 era. This dataset is publicly available covering a considerable period of roughly three months, to allow people with a similar interest in Twitter, Bitcoin, sentiment analysis, and the financial sector to perform research unimpeded.