arrow_back Finding signal in the noise: How to put big data to use
Submitted by Anand S (@sanand0) on Thursday, 29 March 2012
How can text be analysed quantitatively? How can it be visualised? What tools exist today that I can use?
This tutorial will walk through examples, available tools, and the theory behind visualising text.
There are a surprisingly large number of techniques for visualising text have emerged in the last five years. Streamgraphs, document arcs, word spectrums, the ever popular word cloud, etc.
Combined with traditional analytical techniques like sentiment analysis, statistical improbability, stemming, n-gram Markov chains, etc, we now have powerful ways of summarising and extracting meaning from text.
This session will walk through examples of popular text visualisations in an easy-to-understand way, online tools you can use right away, and provide you a starting point to build your own applications to visualise text.
Anand is a data scientist at Gramener, a data visualisation company.