Done!

I've added a gif to the docs and the README.

Author here. Must give credit where it's due:

These patterns come from a talk by Vladimir Keleshev, author of docopt and excellent Pythonista. These are NOT my original work.

Here is the talk: http://www.youtube.com/watch?v=GZNUfkVIHAY

You couldn't say anything to motivates me more about reading the linked article. docopt is a gem ;)

"Any time you catch yourself saying, “Well look at how X big company does it.” or “Well X big company tried that and it didn’t work for them,” please smack yourself in the face."

THANK YOU THANK YOU THANK YOU.

This is an extension to the Python programming language that makes it easier to analyze and manipulate text.

For example, an analyst might use sentiment analysis to see whether Facebook posts about a product are "positive" or "negative" in tone.

As another example, I hacked together this online sentiment analyzer using TextBlob: https://textfeel.herokuapp.com/

See also: NLP (Wikipedia): https://en.wikipedia.org/wiki/Natural_language_processing NLTK (a python library for NLP): http://nltk.org/ Twitter opinion mining using pattern: http://www.clips.ua.ac.be/pages/pattern-examples-elections

OK, so it's mostly to analyze text that's already been written? Can it also write natural language text based on data inputs?

> Can it also write natural language text based on data inputs?

from the features list it doesn't seem to.

What you're referring to is text generated using a Markov Chain algorithm. This will generate text that seems at first glance to be human generated. On closer inspection you'll find that it only follows common linguistic patterns, the actual content is gibberish.

This sounds interesting. Can you specify an example usecase (what would be the input data and how would generated natural language look like), and I will try to see if I can do it.

For example, financial data would be used as input to generate a daily stock market overview. Something along the lines of:

"Today, the Dow hit a high of 16,200, marking the first time it has crossed the 16,000 barrier. blah blah blah, etc"

Basically, use data points to create a market overview where readers wouldn't know that it was computer generated. That's one idea.

I think the only use case is spam. But it's a big one.

Natural language generation is also an NLP task, but this particular library doesn't seem to tackle it at the moment.

Good hack, now I'm following your github!

More info here: https://github.com/sloria/python-subreddit-stats. Not much to it--the script uses BeautifulSoup to scrape user count, and an Openshift app runs the script hourly as a cronjob.

I don't see the scraper (the BeautifulSoup script) at that repo. Did I miss it or is it not committed?

Link was in the README: https://github.com/sloria/datasources/blob/master/scripts/ru...

I have collected my favorite quotes and talking points from this piece, Michael Church's "Don’t waste your time in crappy startup jobs", and Aza Raskin's "Psychological Pitfalls And Lessons of A Designer-Founder".

Here: http://www.stevenloria.com/disillusionment-in-startups-colle...

I intend to add to this list of quotes, and I also welcome others to email me related bits of wisdom.

That is a beautiful website. Is it custom, or is it something I can install myself? Feel free to refuse to answer :P

Thanks. It is this: https://github.com/holman/left with just a few modifications.