Fear and Greed Index is a way to capture the current emotions, greed and fear about the given market. There are fear and greed indices for stocks markets, but also for the crypto market. The latter captures the sentiment expressed towards cryptocurrencies like Bitcoin, Ethereum, Cardano and others.
Most often, one measures the fear and greed index by collecting the social media posts about these coins and then computing sentiment polarity of these posts, using some form of machine learning model that has been trained on sufficient number of social media posts on Twitter, Facebook, Instagram and others.
By focusing on individual coins, like Bitcoin or Altcoins, like Ethereum and others, one can compute individual fear and greed indices, e.g. Ethereum Fear and Greed index or Bitcoin Fear and greed index.
If one wants to know about the sentiment and emotions about the overall market, it is best to use some form of market-cap weighted approach to this.
Ethereum Fear and Greed Index is thus computed, e.g. with latency of 1 hour, by averaging the sentiment of social media posts about Ethereum or its ticker ETH and then computing the percentiles, which map the index value to the five possible states:
– extreme greed
– extreme fear
Sentiment classification machine learning models used for this belong to the class of Natural Language Processing Models, they are essentially text classification models. Other examples of text classification models include product categorization, opinion mining and classification and others. An interesting text classification models is also multi label classification, where one does not predict between mutual exclusive labels, but one can rather predict one or more labels given text.
One application of multi label classification is for example product tagging.
Here is an excellent library that allows multilabel classification: http://manikvarma.org/code/Parabel/download.html