What are they talking about?

Imagine if you could answer that about any person or group, on any subject, in any language!


Check out these live examples of Sherlock at work

GOP Senators

Sherlock maintains and monitors a list of 50 Senators.

Democrat Senators

Currently 49 on a list we curate

Opinion Poll example

Sherlock can exploit a geographical filter which returns Tweets sent from within a user defined circle anywhere on the planet. We tried an open-ended poll across each constituency on mainland Britain. Here's an interactive map with the results.

Grass roots politics 

People's Vote groups in Gloucester and Cheltenham want to lobby their MPs, and call in Sherlock for objective analysis of their Tweets. Constructive dialogue is their goal, so understanding the changing concerns of Richard Graham and Alex Chalk provides a clear advantage.

GOP Representatives

223 Representatives also monitored 24/7

Democrat Representatives

211 on a list we curate

 #UK Conservatives

List by Tweetminster

 #UK Labour

List by Tweetmister


Sherlock finds the common interests that bind groups together, and needs no further information than the words written. It doesn't need, use,  or store private data. Privacy is baked into the design.

Always up to date

If an earthquake occurs, or a new political sensation invades the press, new names will baffle supervised text-mining techniques. Sherlock works from a list of what to ignore, so new stuff gets handled automatically, just so long as it is frequent.


Sherlock has been tried on more than thirty languages, including Arabic and Russian. It is completely grammar agnostic, and will work on any language, human or computer, for which a stop word list can be provided.


Sherlock tries to be as autonomous as possible, requiring just two parameters, how robust the patterns need to be. and how many words will be enough.  For example, the survey takes 75% pattern confidence, and 20 words

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