Social media sentiment analysis has emerged as a new way to generate potential trading signals for a range of assets, including bitcoin.
The technology is relatively new. It uses AI to determine whether a social media post carries a positive or negative tone. By monitoring Twitter, this form of AI-driven analysis can determine the prevailing mood about various issues to gauge whether the price of an asset is more likely to go up than done (or visa versa.)
In this guide, you will learn how you can use Twitter sentiment analysis to trade bitcoin.
Twitter Sentiment Analysis & Bitcoin Price Predictions
The digital asset markets are short on traditional fundamental data. However, it lends itself well to sentiment analysis. More than any traditional asset class, digital asset valuation is investor sentiment-driven.
There is a growing body of data supporting the reliability of sentiment analysis as a predictor of crypto investment returns.
The SEC has recently announced its intention to subscribe to a social media sentiment analysis tool. The digital asset space is likely not the primary target of the SEC’s monitoring efforts. Still, it might be subjected to scrutiny through this new method.
How Does Sentiment Analysis Work?
Twitter sentiment analysis is a three-step process.
- Data extraction uses the Twitter Firehose to grab tweets relevant to a coin. AI steps in right from the beginning. It attaches a sentiment tag to every tweet.
- Evaluation eliminates spam, duplicate posts, and filters the data stream.
- A quantifiable sentiment rating is then derived from the aggregate data.
The exciting bit is the correlation between the price of the tracked asset and this sentiment score. With some coins, this correlation is positive. With a handful of exceptions, it is negative.
This is impossible to accomplish manually. Those looking to trade based on sentiment will thus need a specialized sentiment analysis system.
How Can You Use Social Media Signals for Trading?
Analytics platform TheTIE , for example, establishes complex correlations between variables such as :
- Market capitalization
- Trading volume
- Tweet volume
- Unique data sources (the number of unique Twitter users from whom the tweet volume originates).
- Relative tweet volume (which accounts for periodic up/downswings in tweet volume)
- Daily sentiment momentum
- Sentiment volatility
It then draws actionable conclusions such as:
- Hourly, daily, and long-term sentiment
- One-hour price projection
- Price projection range
- Price projection accuracy, determined by the percentage of times the price remains within the price projection range
Setting alarms on the price projections could provide actionable trading signals.
Valuable Insights for Investors
Social sentiment data can offer interesting peeks into the subtleties of the bitcoin market.
The NVTweet Ratio is defined as the Coin Market Cap/1M/30-day Average Tweet Volume. It has detected what may be the signs of bitcoin’s institutional investor-driven market capitalization increase throughout 2019.
What this means is that bitcoin’s market cap has increased faster than the amount of Twitter-based banter surrounding it. Institutions presumably do not chat about their investments on Twitter. Thus, this may point to their increasing involvement in the space.
What is also interesting is that altcoins have not yet shown a similar NVTweet Ratio evolution.
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