Crypto sentiment analysis has become an important part of the crypto analytics toolbox. One of the problems with cryptocurrencies that unlike stocks, where we have financial statements available to assess the discounted cashflow value of the company, in crypto there is no comparable thing.
For most cryptocurrencies there are no “cashflows to the coin” which means that it is more difficult to assess the value of given cryptocurrency or its blockchain project.
Often the value of cryptocurrency is associated with the number of people using it for storage of value or for transactions. This number of people can be estimated from blockchain data. The general popularity can be however also seen from the social media mentions. E.g. Bitcoin regularly has over 50,000 mentions (filtered for spam and promotional posts) according to BittsAnalytics, leading platform for crypto sentiment analysis and social media mentions analysis of cryptocurrencies.
Another way to determine what users think about cryptocurrencies like Bitcoin, Ethereum, Polkadot and others is to compute sentiment of social media posts about them. This can be done using various machine learning models, a popular one is e.g. Support Vector Machines.
This is an example of how crypto sentiment analysis produced average sentiment for the Bitcoin cryptocurrency:
One can see how, as the price of Bitcoin fell on comments of Elon Musk about energy consumption of Bitcoin, the sentiment of Bitcoin drastically dipped.
BittsAnalytics actually provides a nice feature where you can see which words are most frequently used in positive and negative social media posts about Bitcoin, e.g.