1000 different people, the same words – Textio Word Nerd
▻https://textio.ai/1000-different-people-the-same-words-6149b5a1f351
https://cdn-images-1.medium.com/max/2000/1*J39XhH26c16ubL2mviuv6w.png
In this sample, Textio analyzed 25,060 jobs published over the last year by Amazon, Apple, Facebook, Google, Microsoft, Netflix, Salesforce, Slack, Twitter, and Uber.
Encartopedia
▻http://encartopedia.fastforwardlabs.com
▻http://blog.fastforwardlabs.com/2017/08/15/encartopedia.html
I used #machine_learning techniques and #visualization to explore new navigation possibilities for Wikipedia while preserving its hypertextual feel. With Encartopedia, you can map the path of any journey through Wikipedia, or use the visualization to jump to articles near and far.
http://blog.fastforwardlabs.com/images/2017/08/enc-ui.png
#text_mining #proximité #cartographie_de_n'importe_quoi #hypertexte
(assez proche de ce que je fais avec #tSNE)
De-Jargonizer
▻http://scienceandpublic.com
How accessible is your work? Paste your article or upload a file to analyze the amount of jargon in your writing.
#jargon #text_mining (English and Hebrew only)
#textreuse: This #R package provides a set of functions for measuring #similarity among documents and detecting passages which have been reused. It implements shingled #n-gram, skip n-gram, and other tokenizers; similarity/dissimilarity functions; pairwise comparisons; and minhash and locality sensitive hashing algorithms.
▻https://github.com/lmullen/textreuse
#text_mining