Myths and Realities: Sentiment Analysis for Crypto Assets

The challenges of efficiently leveraging sentiment analysis to evaluate the behavior of an asset are not unique to the crypto space.

Efficiently leveraging sentiment analysis for crypto assets requires machine learning depth and rigor.

Polarity Analysis: This type of sentiment analysis ranks textual sentiment in positive, negative and neutral.

Aspect Sentiment Analysis: This type of sentiment analysis focuses on interpreting the sentiment about specific subjects within a sentence rather than a sentence as a whole.

Looking at the previous list, we can clearly see the benefits of sentiment analysis for crypto assets.

From that perspective, it is only logical to assume NLP techniques such as sentiment analysis can identify alpha or smart beta generator factors to predict the behavior of crypto assets.

From a purely technological standpoint, building effective sentiment analysis models for crypto assets requires models trained in the terminology of crypto markets, but that also analyze news as sources of information and social media feeds as amplifiers of sentiment.

If we get past this technological challenge, we are now faced with one of the biggest psychological misconceptions when comes to sentiment analysis models in the crypto space.

The core principle of sentiment-market impact analysis models would be to contextualize the knowledge of sentiment models to the specifics of the crypto market.

As the markets evolve, we are likely to see a transition from plain sentiment analysis techniques to more holistic market impact models that quantify the relevance of specific topics in the behavior of the crypto markets.