The Senno Core is able to generate two highly accurate variables using its algorithm. The algorithm could recommend analyses, taking several variables into account, such as:
According to Senno, “We take both ΔB and ΔM into account when creating accurate real time analysis recommendations, making sure to have the lowest possible intervals between samples. Our algorithm will measure the change in buzz and the mood of each minute.
For example, we ran an analysis on the subject ‘Bitcoin’ on 31 Mar 2017 when Japan’s Financial Services Agency (FSA) published an announcement at 12:00pm saying that Bitcoin will soon become an official currency in Japan. We expected to observe a huge positive Buzz regarding Bitcoin, so we looked at Senno’s output at 12:15pm. We measured the change in BTC Mood for the last 15 minutes in comparison to the average 15-minute intervals in the previous 24-hour period. We found the following data:
Current Buzz(B) for the last 15 minutes = 1M mentions
Avg B (measured in 5-min intervals over 24h) = 180K mentions
Avg ΔB (24h) = 20K comments
ΔB (B- (Avg B + Avg ΔB)) = 800K comments
BTC token had a 5x buzz (B) change rising from max 200K avg (180K+20K std) references in 15 minutes to 1M in the last 15m (12:00-12:15). Out of the 1M references 700K are positive moods(P), 200K are negative moods(N) and 100K are neutral sentiments. Thus the signal will be:
Now, those are indications of a Strong Positive Upward reference trend within a 15-minute timeframe.
Here’s what happened with Bitcoin (BTCUSD).
That in mind, the Senno platform could be helpful in predicting changes in trends, which would be helpful to nearly all companies and users on the platform.