Extract Ernst Hemingway Quotes from Goodreads

Here’s a fast and simple process to extract Ernst Hemingway Quotes from Goodreads. The process is not done, I still need to loop over each quote and add 1 day to the %{now} macro. The goal is to then write them in markdown with %{now}+1 day and auto schedule them on my other website (thomasott.io).

Right now the Goodreads.com web structure is easy to extract but I suspect they’ll make it harder one day.


Keras and NLTK

Lately I’ve been doing a lot more Python hacking, especially around text mining and using the deep learning library Keras and NLTK. Normally I’d do most of my work in RapidMiner but I wanted to do some grunt work and learn something along the way.  It was really about educating myself on Recurrent Neural Networks (RNN) and doing it the hard way I guess.

Keras and NLTK

As usually I went to google to do some sleuthing about how to text mine using an LSTM implementation of Keras and boy did I find some goodies.

The best tutorials are easy to understand and follow along. My introduction to Deep Learning with Keras was via Jason’s excellent tutorial called Text Generation with LSTM Recurrent Neural Networks in Python with Keras.

Jason took a every easy to bite approach to implementing Keras to read in the Alice In Wonderland book character by character and then try to generate some text in the ‘style’ of what was written before. It was a great Proof of Concept but fraught with some strange results. He acknowledges that and offers some additional guidance at the end of the tutorial, mainly removing punctuation and more training epochs.

The text processing is one thing but the model optimization is another. Since I have a crappy laptop I can just forget about optimizing a Keras script, so I went the text process route and used NLTK.

Now that I’ve been around the text mining/processing block a bunch of times, the NLTK python library makes more sense in this application. I much prefer using the RapidMiner Text Processing implementation for 90% of what I do with text but every so often you need something special and atypical.

Initial Results

The first results were terrible as my tweet can attest too!

So I added a short function to Jason’s script that preprocesses a new file loaded with haikus. I removed all punctuation and stop words with the express goal of generating haiku.

While this script was learning I started to dig around the Internet for some other interesting and related posts on LSTM’s, NLTK and text generation until I found Click-O-Tron.  That cracked me up. Leave it to us humans to take some cool piece of technology and implement it for lulz.


I have grandiose dreams of using this script so I would need to put it in production one day. This is where everything got to be a pain in the ass. My first thought was to run the training on  a smaller machine and then use the trained weights to autogenerate new haikus in a separate scripts. This is not an atypical type of implementation. Right now I don’t care if this will take days to train.

While Python is great in many ways, dealing with libraries on one machine might be different on another machine and hardware. Especially when dealing with GPU’s and stuff like that.  It’s gets tricky and annoying considering I work on many different workstations these days. I have a crappy little ACER laptop that I use to cron python scripts for my Twitter related work, which also happens to be an AMD processor.

I do most of my ‘hacking’ on larger laptop that happens to have an Intel processor. To transfer my scripts from one machine to another I have to always make sure that every single Python package is installed on each machine. PITA!

Despite these annoyances, I ended up learning A LOT about Deep Learning architecture, their application, and short comings. In the end, it’s another tool in a Data Science toolkit, just don’t expect it to be a miracle savior.

Additional reading list

  • http://h6o6.com/2013/03/using-python-and-the-nltk-to-find-haikus-in-the-public-twitter-stream/
  • https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py

The Python Script




Using Python with RapidMiner

Just a quick note, I recently recorded a new video on how to use Python with RapidMiner Studio. This was part of the “Everyday Data Science with Tom Ott” series and you can check out more of my RapidMiner videos here.

Using Python with RapidMiner

In this video I show you how to use the Twython package and RapidMiner’s Text Mining extension to load Twitter tweets, text process them, and post a retweet based on your text processed data!

Update: This video and process was made prior to the introduction of the native Twitter operators, but the original strategy remains.

Update2: As of late 2017, this video is no longer available on RapidMiner’s YouTube channel. I do not have access to the video BUT I do have the sample process.  Just replace the app_key, app_secrete, oauth_token, and oauth_secrete with your own keys.