running-MALLET.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MALLET by Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Normally MALLET is run on the unix command line. I am running MALLET in an IPython Notebook so I can *share and explain my work.* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, we need to have some text for MALLEt to chew on. I've taken the easy way out and already prepared a set of documents for modeling. These are in the `data/` folder in this repository. \n",
    "\n",
    "There are 191 text files in that directory and each one represents a the text content of a blog post selected by the editors of [DHNOW](http://digitalhumanitiesnow.org/). Unfortunately, I've not included the metadata about which text document belongs to which post, oh well. ¯\\\\_(ツ)_/¯\n",
    "\n",
    "What I can say is that I used the [Diffbot](http://diffbot.com) article extraction API to pull out just the text content from the blog posts (so no menu, about-me boilerplate, or comments). It is important to know from whence the data comes!!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 4648\n",
      "-rw-r--r--@ 1 mcburton  staff    16K Aug 12  2012 1.txt\n",
      "-rw-r--r--@ 1 mcburton  staff   7.5K Aug 12  2012 10.txt\n",
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      "-rw-r--r--@ 1 mcburton  staff   179B Aug 12  2012 108.txt\n",
      "-rw-r--r--@ 1 mcburton  staff     0B Aug 12  2012 109.txt\n",
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     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls -lh data/ # This command lists the text files in the data directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Lets take a look at what is inside one of these blog posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barbara Taranto\n",
      " Digital Program Director\n",
      " New York Public Library\n",
      "The New York Public Library recently launched its first foray into crowd sourcing metadata by exposing 40,000 image pages of turn of the century restaurant and cruise ship menus: “What’s On the Menu?” The goal of the project was to widely distribute the transcription of the menu items into a structured and reusable form. The site was exceedingly popular in its first few months.\n",
      "Recent activity has flattened somewhat, raising issues regarding the public’s appetite for these projects. More importantly, the menus project raised hard questions about the quality of the crowd sourced content, the longevity of the data, and the disposition of the data (e.g. What is it? Is it good enough for our purposes? Should we keep it? If yes, where does it belong?).\n",
      "This presentation will discuss these issues and propose some alternative views on metadata, user-generated content, and the intersection of the two.\n",
      "http://menus.nypl.org/"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cat data/55.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading MALLET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[MALLET](http://mallet.cs.umass.edu/index.php) isn't crazy complicated infrastructure, it is just a Java application written by [David Mimno](https://twitter.com/dmimno). So you have to have Java installed, but otherwise there are no technical dependencies. \n",
    "\n",
    "However, you need to understand the command line in order to use it (and you need to understand what it does). MALLET is not a \"plug and play\" tool, it has no graphical user interface, and, while being pretty well documented, it doesn't hold your hand.\n",
    "\n",
    "The commands below automatically download and unzip version 2.0.7 of MALLET. You can also [download](http://mallet.cs.umass.edu/download.php) MALLET separately and unzip it yourself. The rest of this tutorial expects the MALLET application to be in the `mallet-2.0.7/` directory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "curl http://mallet.cs.umass.edu/dist/mallet-2.0.7.zip > mallet.zip # download the zip archive\n",
    "unzip mallet.zip #unzip the archive into the current directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets test to make sure MALLET is working correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "mallet-2.0.7/bin/mallet # run the mallet executable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We just ran the MALLET program, but we didn't give it any commands so it complained with `Unrecognized command` and then spit out the list of commands it expects. That means it is working! Now we can actually do something!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Mallet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have installed MALLET and we have some data, we can start doing some topic modeling. 😊\n",
    "\n",
    "Topic modeling with MALLET involves two steps: \n",
    "1. importing your data\n",
    "2. training the model\n",
    "\n",
    "Importing the data involves re-shaping the narrative text into a bag-of-words. It also performs some cleaning such as removing stopwords. \n",
    "\n",
    "Training the model is the \"topic modeling\" part of the exercise that performs the algorithmic magic to tell you \"what it all means\" (not really tho...). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one final bit of data fitness that needs to happen for us to be able to topic model these blog posts. We need to \"import\" the text files using the MALLET application and create a .mallet file. Here we will import all the text files in the `data/` directory using the `import-dir` command. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Labels = \n",
      "   data\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mallet-2.0.7/bin/mallet import-dir --input data \\\n",
    "--output documents.mallet \\\n",
    "--keep-sequence \\\n",
    "--remove-stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 33032\n",
      "-rw-r--r--@   1 mcburton  staff   292K Jul  1 09:46 Data work.png\n",
      "-rw-r--r--@   1 mcburton  staff   158K Jul  1 09:47 Data-science-workflow.png\n",
      "-rw-r--r--@   1 mcburton  staff   288K Jul  1 09:47 Real workflow.png\n",
      "drwxr-xr-x@ 194 mcburton  staff   6.4K Jun 30 15:35 data\n",
      "-rw-r--r--@   1 mcburton  staff   1.1M Apr  4  2014 dh-blog-map-small.jpeg\n",
      "-rw-r--r--@   1 mcburton  staff   815K Jul  1 11:17 documents.mallet\n",
      "-rw-r--r--@   1 mcburton  staff    96K Jul  1 09:48 estimating.png\n",
      "-rw-r--r--@   1 mcburton  staff    16K Jul  1 09:59 intro-to-topic-modeling.ipynb\n",
      "-rw-r--r--@   1 mcburton  staff   232K Jul  1 09:57 intro-to-topic-modeling.slides.html\n",
      "-rw-r--r--@   1 mcburton  staff   1.4K Jun 30 17:07 keywords.txt\n",
      "drwx------@  15 mcburton  staff   510B Jul  1 11:12 mallet-2.0.7\n",
      "-rw-r--r--@   1 mcburton  staff    13M Jun 30 16:49 mallet.zip\n",
      "-rw-r--r--    1 mcburton  staff    22K Jul  1 11:17 running-MALLET.ipynb\n",
      "-rw-r--r--@   1 mcburton  staff   171K May  9  2013 word-distribution2.png\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls -lh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This created a new file, `documents.mallet` in the current directory. This is the file that we will feed back to the MALLET application to \"train\" a topic model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the data imported and *in shape* we can finally do the topic modeling part of topic modeling. The command below trains a model on the text with 10 topics. You can tweak that number by changing the `--num-topics` parameter. Additionally, we are writing those keywords to a file (rather than just spitting them out on the command line) with the `--output-topic-keys` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded.\n",
      "Coded LDA: 100 topics, 7 topic bits, 1111111 topic mask\n",
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      "\n",
      "<1000> LL/token: -8.92493\n",
      "\n",
      "Total time: 25 seconds\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!mallet-2.0.7/bin/mallet train-topics \\\n",
    "--input documents.mallet \\\n",
    "--num-topics 100 \\\n",
    "--output-topic-keys keywords.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats, you just modeled some topics. Lets take a look at the `keywords.txt` file and see if we can get any insights about the digital humanities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "92\t0.5\tarchives archival archivists web american mid conference speaker society august materials twitter silence atlantic archive regional records annual material \r\n",
      "93\t0.5\tgoogle books century article scholars time cohen digitization authors mass story published culturomics copyright kind issue dan nineteenth journal \r\n",
      "94\t0.5\tstudents class teaching student teach classroom major seminar experience working classes ideas college pedagogical youtube write undergraduate readings fall \r\n",
      "95\t0.5\tdigital humanities work research tools scholars methods ways humanistic humanists intellectual field scholarly end methodological creation writing journals innovative \r\n",
      "96\t0.5\tevidence interpretation set produce aesthetic response reader concept artifacts arguments act audiences add concepts artifact ian potential assumptions forums \r\n",
      "97\t0.5\tpoint case kind broader order kinds defined include suggest differences technical identify based making established position subject claims increasingly \r\n",
      "98\t0.5\tfind understand learn great necessarily humanists statistics communication similar times points general part task objects understanding read feel impossible \r\n",
      "99\t0.5\twork part process nature thinking issues point made kind audience study continue role present professional complex fact address effect \r\n"
     ]
    }
   ],
   "source": [
    "cat keywords.txt"
   ]
  },
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   "source": [
    "There are other kinds of output you can get from MALLET. Besides the keywords, the other data most people use in their analysis are the document-topics mixtures. These data indicate the expression of each topic within a document. You can use this information to \"cluster\" documents based upon the the proportion of a topic in the set of documents. For an example, take a look at [a particular topic in the dfr-browser](http://agoldst.github.io/dfr-browser/demo/#/topic/4) or in [Mining the Dispatch](http://dsl.richmond.edu/dispatch/Topics/view/15)"
   ]
  },
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