Standard and Poor's Downgrades US Banks

datasift | 30th November 2011

Here's a filter that collects comments and sentiment on Standard and Poor's downgrade of US banks. With DataSift, it's easy to filter out the insignificant content and focus on the things that are being retweeted, the thoughts from key players, and the comments that have strong sentiment.

tag "positive" {salience.content.sentiment > 3 or salience.title.sentiment > 3}
tag "negative" {salience.content.sentiment < -3 or salience.title.sentiment < -3}

return {
   interaction.content contains_any "credit rating, credit ratings, S&P, Standard and Poor\'s"
   and 
   (
       interaction.content contains_any "bank, banks"
       or interaction.content contains_any "Bank of America, Wells Fargo, Goldman Sachs, Citigroup, JP Morgan Chase, New York Mellon"
   )
   and language.tag == "en" and
   (
      klout.score > 40 or
      links.retweet_count > 5 or
      salience.content.sentiment > 3 or
      salience.title.sentiment > 3 or
      salience.content.sentiment < -3 or
      salience.title.sentiment < -3
   )
}

It delivers posts that include "credit rating(s)" and mention "bank(s)" or six specific major US banks.

It restricts the output to posts written in English.

It restricts the output to posts that include some non-trivial sentiment, or posts that have been retweeted more than five times, or posts written by authors with a significant Klout score.

And, finally, it tags each object for positive or negative sentiment.

The cost is 1.1 DPU which means that this stream is far from expensive to run. If you ran it for an hour and it delivered 1,000 Tweets, the price would be:

  1.1 * 0.20 + 1000 * 0.0001 = $0.32

What you can do next

Look at the messages here: http://datasift.com/stream/15114/us-bank-credit-ratings#app1-preview

Look at the charts here: http://datasift.com/stream/15114/us-bank-credit-ratings#app4-sentiment

Or use DataSift's powerful API to consume this data and perform your own processing on it, such as post-processing those sentiment tags that we added.


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