Hi folks, before Christmas, I had some very informal talks with some other Kibble folks in various places about Key Phrase Extraction (KPE) and how we might be able to use that in Kibble.
KPE is the process of taking a longer text, for instance an email, and extracting small sentences or words that are at the center of the text - as an example, taking chapter one from a Christmas Carol would (among other things) point to Scrooge, Marley and 'Dead as a Door-nail' as key elements. This isn't to say that you can always grasp exactly what all 10,000 words in that chapter was about, but you get some elements that play a key role in it. As an aside, KPE is supported by most text analysis services out there (I've tried it with Watson, Azure and picoAPI thus far) My idea with KPE is so generate a list of keywords and sentences to accompany each email (and possibly issues/tickets?) so that we can generate a map of "hot topics" both in projects and across them, looking for commonalities and trends. This _could_ show that most projects have similar work-flows/activities in common, or it could show the exact opposite - I don't know yet, but I sure would love to find out! It could perhaps also tell us (ngram-style) something about how technology progresses over time by following the occurrences of certain words/sentences in projects as a time-series. I would _love_ to get some input on this (especially as far as 'what can we use this for, if anything' is concerned), and I will probably start some basic KPE extraction tests in the coming days. As mentioned elsewhere, the Kibble demo instance has virtually unlimited text analysis credits at the moment, including KPE, so we might as well make use of that :) WDYT? With regards, Daniel.