Social Sentiment & Activity
Daily Sentiment Score & 7 Day Moving Average
Popular Topics & Highlights
... @Spotify stats get weirder and weirder every year?
... shared some of the best with @billieeilish #SpotifyWrapped
2019 has been a wild year. Releasing a new record, touring with @alicecooper, headliners, & we're almost done. Time has flown by. Thanks everyone who spent time helping us reach our highest numbers on @Spotify yet. Happy to hear you've enjoyed the ...
Thank you Gabbie Hanna for spending 1 hours with me this year on @Spotify. ...
Thank you @postmalone for spending 15 hours with me this year on @Spotify. You are my #1. 🎉 🎉 i love u!!! 💕 ...
... to my music more than 3 million hours on @Spotify. That's a whole lot of Country music!!! #SpotifyWrapped
Listen to our new single "Black Swan" on ...
... healthcare: - Pay $9.99/mo premium for some music - Pay $7 per song listen til you spend ...
... teamed up again on my new Ones to Watch 2020 playlist on @Spotify. Have a listen on your ...
... download The Gift on iTunes for just 59p or stream it on Spotify 🙌
Yesterday, Sunday, January 26, we saw positive social sentiment for Spotify Technology S.A. (SPOT). We gave it a score of 46. There was a significant amount of activity, such as likes and shares, on social media.
Over the prior 7 days we calculated an average sentiment score of 33 (positive). Over the prior 14 days our AI scored an average sentiment of 36 (positive). And for the prior 30 days our social sentiment score for Spotify Technology S.A. (SPOT) is 38 (positive).
We believe social network activity can help inform investors of the positive and negative opinions people hold of a company. Social media posts are made both before and after market price fluctuations. We can make no claim of correlation or causation between market prices and social media sentiment. We make no evaluation of stock value and we can make no prediction of future prices. The information gathered on SocialSentiment.io should be only one part of a broader investment strategy.
We've trained our artificial intelligence systems from past social media posts. While tests show our predictions are usually accurate to our models, a human may come to a different conclusion for some posts.