CryptoTheorem Launches News Sentiment Analysis Service

AMSTERDAM (May 28, 2018) – CryptoTheorem (, the leading provider of real-time cryptocurrency sentiment analysis services, has launched a new version of its Sentiment Circles service which, in addition to nearly doubling the number of cryptocurrency events tracked, now detects and analyzes social chatter on global scale as they happen.

CryptoTheorem ( Sentiment Analytics will now automatically detect news events and sentiment on every major geopolitical location, government organization, company, cryptocurrency or asset token. CryptoTheorem’s deep learning algorithms sift through thousands of unstructured social chatter in nanoseconds searching for information likely to impact financial markets. Over 1,200 macroeconomic, geopolitical, and token events are systematically detected and the analysis delivered as a real-time data feed.

“For the first time, you can automatically react to unexpected events and act first within milliseconds of them being reported.”, said Maxim Tartarovsky, Spokesperson of CryptoTheorem team.

“Historically, such event detection relied on manual tagging which risked inconsistency and delays, but we have now developed a leading solution that delivers value, regardless of asset classes or how short the investment horizon is. The only limitation is the number of scenarios and models our users can imagine”, concluded Maxim Tartarovsky.

About CryptoTheorem

CryptoTheorem is a leading provider of real-time social chatter sentiment analysis services. Professionals rely on CryptoTheorem for its speed and accuracy in analyzing large amounts of unstructured social chatters and transforming it into real-time actionable data to support trading, investment and risk management models and decisions. Traders use CryptoTheorem’s services to generate better returns by systematically incorporating the effect of social chatter in their trading, investment or risk management processes.

To learn more about CryptoTheorem, visit or e-mail [email protected]