Sentiment metrics

    Our Sentiment Metrics

    • Positive/Negative Sentiment - Shows how many mentions of a term/asset are expressed in a positive/negative manner.
    • Sentiment Balance - The difference between Positive Sentiment and Negative Sentiment
    • Sentiment Weighted - An improved version of the Sentiment Balance that adjusts the values by considering the number of mentions, standardizing data to make diverse asset sentiments comparable.

    What is Sentiment?

    Sentiment is the attitude, thought or judgement that people have which is based on their feelings.

    Positive sentiment is an attitude that is hopeful, confident, and considering of the good aspects of a situation or a subject. In the context of cryptocurrencies this can manifest as optimism about the future of a coin, hope that the price will increase, belief in the success of a token, and many more.

    Negative sentiment is an attitude that is critical, disapproving, and considering of the bad aspects of a situation or a subject. In the context of cryptocurrencies this can manifest as expressing the opinion that a token is a scam, belief that a coin will fail, and many more.

    Sentiment Analysis is the problem of computationally identifying and categorizing emotions, opinions and subjective information in a given piece of text.


    Please note that metrics may undergo changes in historical values due to automated recalculations triggered monthly. We constantly update our labels which helps us to keep labels as fresh as possible but result historical data changes. Any modifications to labels, social sources, or relevant jobs will prompt recalculation for the previous month's data. Within a 12 hour period, metric can be supplemented with new data.

    Sentiment Score

    We trained a machine learning model on a large Twitter dataset that contains over 1.6 million tweets, each labelled as either positive or negative. The model is then used to evaluate the sentiment of each single document in the Social Data set it assigns a positive and negative sentiment score to each message/post/comment/etc.

    The score is the probability that the content of the text is positive or negative respectively. Therefore both the positive and negative sentiment scores fall in a range between 0 (not positive/negative at all) and 1 (extremely positive/negative). Moreover, the sum of these two scores always equals 1.


    I'm really excited about the new Libra currency!

    This message has a positive score of 0.75 and a negative score of 0.25.

    We use this approach for messages and comments from social networks conversations because the structure of the text there is usually more or less the same: short messages with a single and/or simple idea behind them.

    But this is not the case for all the messages: some of them might be long and complicated, some might be just neutral or contain spam or other irrelevant information. These kind of messages usually have a pretty vanished pair of sentiment scores: both positive and negative scores are close to 0.5. We don't include these kind of messages while calculating the Sentiment Metrics: they are filtered out by a certain threshold.

    Metrics Calculation

    To ensure relevance and accuracy, only text with a sentiment score above a certain threshold is considered in our sentiment metrics. This approach filters out neutral, spam, or irrelevant messages, focusing on the most impactful data. Our sentiment metrics are recalculated monthly to account for any changes in our models or data sources, providing you with the most up-to-date insights.