Twitter NetworkX IPython Notebook

text_utils.py

"""
Utilities for simple text analysis: word frequencies and co-occurrence graph.

These tools can be used to analyze a plain text file treating it as a list of
newline-separated sentences (e.g. a list of paper titles).

It computes word frequencies (after doing some naive normalization by
lowercasing and throwing away a few overly common words).  It also computes,
from the most common words, a weighted graph of word co-occurrences and
displays it, as well as summarizing the graph structure by ranking its nodes in
descending order of eigenvector centrality.

This is meant as an illustration of text processing in Python, using matplotlib
for visualization and NetworkX for graph-theoretical manipulation.  It should
not be considered production-strength code for serious text analysis.

Author: Fernando Perez <fernando.perez@berkeley.edu>
"""

#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------

# From the standard library
import os
import re
import urllib2

# Third-party libraries
import networkx as nx
import numpy as np

from matplotlib import pyplot as plt

#-----------------------------------------------------------------------------
# Function definitions
#-----------------------------------------------------------------------------

def rescale_arr(arr, amin, amax):
    """Rescale an array to a new range.

    Return a new array whose range of values is (amin, amax).

    Parameters
    ----------
    arr : array-like

    amin : float
      new minimum value

    amax : float
      new maximum value

    Examples
    --------
    >>> a = np.arange(5)

    >>> rescale_arr(a,3,6)
    array([ 3.  ,  3.75,  4.5 ,  5.25,  6.  ])
    """

    # old bounds
    m = arr.min()
    M = arr.max()
    # scale/offset
    s = float(amax-amin)/(M-m)
    d = amin - s*m

    # Apply clip before returning to cut off possible overflows outside the
    # intended range due to roundoff error, so that we can absolutely guarantee
    # that on output, there are no values > amax or < amin.
    return np.clip(s*arr+d,amin,amax)


def all_pairs(items):
    """Make all unique pairs (order doesn't matter)"""
    pairs = []
    nitems = len(items)
    for i, wi in enumerate(items):
        for j in range(i+1, nitems):
            pairs.append((wi, items[j]))
    return pairs


def removal_set(words, query):
    """Create a set of words for removal for a given query."""
    rem = set(words.split())
    qw = [w.lower()  for w in query.split()]
    for w in qw:
        rem.add(w)
        rem.add('#' + w)
        qq = ''.join(qw)
        rem.add(qq)
        rem.add('#' + qq)
    return rem

def lines_cleanup(lines, min_length=4, remove = None):
    """Clean up a list of lowercase strings of text for simple analysis.

    Splits on whitespace, removes all 'words' less than `min_length` characters
    long, and those in the `remove` set.

    Returns a list of strings.
    """
    remove = set(remove) if remove is not None else []
    filtered = []
    for line in lines:
        a = []
        for w in line.lower().split():
            wnorm = w.rstrip('.,:').replace('[', '').replace(']', '')
            if len(wnorm) >= min_length and wnorm not in remove:
                a.append(wnorm)
        filtered.append(' '.join(a))
    return filtered


def print_vk(lst):
    """Print a list of value/key pairs nicely formatted in key/value order."""

    # Find the longest key: remember, the list has value/key paris, so the key
    # is element [1], not [0]
    longest_key = max([len(word) for word, count in lst])
    # Make a format string out of it
    fmt = '%'+str(longest_key)+'s -> %s'
    # Do actual printing
    for k,v in lst:
        print fmt % (k,v)


def word_freq(text):
    """Return a dictionary of word frequencies for the given text.

    Input text should be given as an iterable of strings."""

    freqs = {}
    for word in text:
        freqs[word] = freqs.get(word, 0) + 1
    return freqs


def sort_freqs(freqs):
    """Sort a word frequency histogram represented as a dictionary.

    Parameters
    ----------
    freqs : dict
      A dict with string keys and integer values.

    Return
    ------
    items : list
      A list of (count, word) pairs.
    """
    items = freqs.items()
    items.sort(key = lambda wc: wc[1])
    return items


def summarize_freq_hist(freqs, n=10):
    """Print a simple summary of a word frequencies dictionary.

    Paramters
    ---------
    freqs : dict or list
      Word frequencies, represented either as a dict of word->count, or as a
      list of count->word pairs.

    n : int
      The number of least/most frequent words to print.
    """

    items = sort_freqs(freqs) if isinstance(freqs, dict) else freqs
    print 'Number of unique words:',len(freqs)
    print
    print '%d least frequent words:' % n
    print_vk(items[:n])
    print
    print '%d most frequent words:' % n
    print_vk(items[-n:])


def co_occurrences(lines, words):
    """Return histogram of co-occurrences of words in a list of lines.

    Parameters
    ----------
    lines : list
      A list of strings considered as 'sentences' to search for co-occurrences.

    words : list
      A list of words from which all unordered pairs will be constructed and
      searched for co-occurrences.
    """
    wpairs = all_pairs(words)

    # Now build histogram of co-occurrences
    co_occur = {}
    for w1, w2 in wpairs:
        rx = re.compile('%s .*%s|%s .*%s' % (w1, w2, w2, w1))
        co_occur[w1, w2] = sum([1 for line in lines if rx.search(line)])

    return co_occur


def co_occurrences_graph(word_hist, co_occur, cutoff=0):
    """Convert a word histogram with co-occurrences to a weighted graph.

    Edges are only added if the count is above cutoff.
    """
    g = nx.Graph()
    for word, count in word_hist:
        g.add_node(word, count=count)
    for (w1, w2), count in co_occur.iteritems():
        if count<=cutoff:
            continue
        g.add_edge(w1, w2, weight=count)
    return g

# Hack: offset the most central node to avoid too much overlap
rad0 = 0.3

def centrality_layout(wgraph, centrality):
    """Compute a layout based on centrality.
    """
    # Create a list of centralities, sorted by centrality value
    cent = sorted(centrality.items(), key=lambda x:float(x[1]), reverse=True)
    nodes = [c[0] for c in cent]
    cent  = np.array([float(c[1]) for c in cent])
    rad = (cent - cent[0])/(cent[-1]-cent[0])
    rad = rescale_arr(rad, rad0, 1)
    angles = np.linspace(0, 2*np.pi, len(centrality))
    layout = {}
    for n, node in enumerate(nodes):
        r = rad[n]
        th = angles[n]
        layout[node] = r*np.cos(th), r*np.sin(th)
    return layout


def plot_graph(wgraph, pos=None, fig=None, title=None):
    """Conveniently summarize graph visually"""

    # config parameters
    edge_min_width= 3
    edge_max_width= 12
    label_font = 18
    node_font = 22
    node_alpha = 0.4
    edge_alpha = 0.55
    edge_cmap = plt.cm.Spectral

    # Create figure
    if fig is None:
        fig, ax = plt.subplots()
    else:
        ax = fig.add_subplot(111)
    fig.subplots_adjust(0,0,1)

    # Plot nodes with size according to count
    sizes = []
    degrees = []
    for n, d in wgraph.nodes_iter(data=True):
        sizes.append(d['count'])
        degrees.append(wgraph.degree(n))

    sizes = rescale_arr(np.array(sizes, dtype=float), 100, 1000)

    # Compute layout and label edges according to weight
    pos = nx.spring_layout(wgraph) if pos is None else pos
    labels = {}
    width = []
    for n1, n2, d in wgraph.edges_iter(data=True):
        w = d['weight']
        labels[n1, n2] = w
        width.append(w)

    width = rescale_arr(np.array(width, dtype=float), edge_min_width, 
                        edge_max_width)

    # Draw
    nx.draw_networkx_nodes(wgraph, pos, node_size=sizes, node_color=degrees,
                           alpha=node_alpha)
    nx.draw_networkx_edges(wgraph, pos, width=width, edge_color=width,
                           edge_cmap=edge_cmap, alpha=edge_alpha)
    nx.draw_networkx_edge_labels(wgraph, pos, edge_labels=labels, 
                                 font_size=label_font)
    nx.draw_networkx_labels(wgraph, pos, font_size=node_font, font_weight='bold')
    if title is not None:
        ax.set_title(title, fontsize=label_font)
    ax.set_xticks([])
    ax.set_yticks([])

    # Mark centrality axes
    kw = dict(color='k', linestyle='-')
    cross = [ax.axhline(0, **kw), ax.axvline(rad0, **kw)]
    [ l.set_zorder(0) for l in cross]


def plot_word_histogram(freqs, show=10, title=None):
    """Plot a histogram of word frequencies, limited to the top `show` ones.
    """
    sorted_f = sort_freqs(freqs) if isinstance(freqs, dict) else freqs

    # Don't show the tail
    if isinstance(show, int):
        # interpret as number of words to show in histogram
        show_f = sorted_f[-show:]
    else:
        # interpret as a fraction
        start = -int(round(show*len(freqs)))
        show_f = sorted_f[start:]

    # Now, extract words and counts, plot
    n_words = len(show_f)
    ind = np.arange(n_words)
    words = [i[0] for i in show_f]
    counts = [i[1] for i in show_f]

    fig = plt.figure()
    ax = fig.add_subplot(111)

    if n_words<=20:
        # Only show bars and x labels for small histograms, they don't make
        # sense otherwise
        ax.bar(ind, counts)
        ax.set_xticks(ind)
        ax.set_xticklabels(words, rotation=45)
        fig.subplots_adjust(bottom=0.25)
    else:
        # For larger ones, do a step plot
        ax.step(ind, counts)

    # If it spans more than two decades, use a log scale
    if float(max(counts))/min(counts) > 100:
        ax.set_yscale('log')

    if title:
        ax.set_title(title)
    return ax


def summarize_centrality(centrality):
    c = centrality.items()
    c.sort(key=lambda x:x[1], reverse=True)
    print '\nGraph centrality'
    for node, cent in c:
        print "%15s: %.3g" % (node, float(cent))

TwitterNetworkX.ipynb

{
 "metadata": {
  "name": "TwitterNetworkX"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Analysis of Twitter stream data with the IPython Notebook"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this example, we use the IPython notebook to mine data from Twitter with the [Twython library](https://github.com/ryanmcgrath/twython).  Once we have fetched the raw stream for a specific query, we will at first do some basic word frequency analysis on the results using Python's builtin dictionaries, and then we will use the excellent [NetworkX](http://networkx.lanl.gov) library developed at Los Alamos National Laboratory to look at the results as a network and understand some of its properties.  \n",
      "\n",
      "Using NetworkX, we aim to answer the following questions: for a given query, which words tend to appear together in tweets, and global pattern of relationships between these words emerges from the entire set of results?\n",
      "\n",
      "Obviously the analysis of text corpora of this kind is a complex topic at the intersection of natural language processing, graph theory and statistics, and here we do not pretend to provide an exhaustive coverage of it.  Rather, we want to show you how with a small amount of easy to write code, it is possible to do a few non-trivial things based on real-time data from the Twitter stream.  Hopefully this will serve as a good starting point;  for further reading you can find in-depth discussions of analysing social network data in Python in the book [Mining the Social Web](http://shop.oreilly.com/product/0636920010203.do).\n",
      "\n",
      "$$ F = ma $$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Initialization and libraries"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We start by loading the pylab plot support and selecting our figure size to be a bit different than the automatic defaults."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "plt.rc('figure', figsize=(8, 5))\n",
      "import networkx as nx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we load a local library with some analysis utilities whose code is a bit long to display inline. The python module is called `text_utils.py` and can be [downloaded here](text_utils.py)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import text_utils as tu  # shorthand for convenience"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, we'll need to use the free [Twython library](https://github.com/ryanmcgrath/twython) to query Twitter's stream:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from twython import Twython\n",
      "# Create the main Twitter object we'll use later for all queries\n",
      "twitter = Twython()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Query declaration"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we define which query we want to perform, as well as which words we want to filter out from our analysis because they appear very commonly and we're not interested in them.  \n",
      "\n",
      "Typically you want to run the query once, and after seeing what comes out, fine-tune the removal list, as which words are 'noise' is fairly query-specific (and also changes over time, depending on what's happening out there on Twitter):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "query = \"big data\"\n",
      "words_to_remove = \"\"\"with some your just have from it's /via & that they your there this\"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Perform query to Twitter servers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the cell that actually fetches data from Twitter.  We limit the output to the first 30 pages of search max (typically Twitter stops returning results before that)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_pages = 30\n",
      "\n",
      "results = []\n",
      "retweets = []\n",
      "for page in range(1, n_pages+1):\n",
      "    search = twitter.search(q=query+' lang:en', page=str(page))\n",
      "    res = search['results']\n",
      "    if not res:\n",
      "        print 'Stopping at page:', page\n",
      "        break\n",
      "        \n",
      "    for t in res:\n",
      "        if t['text'].startswith('RT '):\n",
      "            retweets.append(t)\n",
      "        else:\n",
      "            results.append(t)\n",
      "            \n",
      "tweets = [t['text'] for t in results]\n",
      "\n",
      "# Quick summary\n",
      "print 'query:   ', query\n",
      "print 'results: ', len(results)\n",
      "print 'retweets:', len(retweets)\n",
      "print 'Variable `tweets` has a list of all the tweet texts'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Stopping at page: 23\n",
        "query:    big data\n",
        "results:  196\n",
        "retweets: 134\n",
        "Variable `tweets` has a list of all the tweet texts\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Text statistics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's see what the first 10 tweets look like:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweets[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[u'Fairy tales are hard to write. \\u201c@samplereality: Big data often reduces complexity in a way similar to bedtime stories nd fairy tales...\\u201d',\n",
        " u'Forbes on impact of #BigData analytics across a kaleidoscopic plethora of industries http://t.co/PRQD8NU1',\n",
        " u'Pre-FAQ Why fast skip for LZF? To suppo fast Range access (over HTTP) on big compressed data',\n",
        " u'Big Rogers Problems: @TechCrunch is even reporting about it! Internet and phone data down: http://t.co/7tssVP2G',\n",
        " u'CDNs Impact Value Chain for Big Data and Media Content Delivery http://t.co/zGa2Zkxn #photography #dslr',\n",
        " u'Big Bets On Big Data - #BigData and #datascience will become the new \"hot\" discipline for the next generation - http://t.co/s5bRIHix',\n",
        " u'Business schools and digital humanities: big data, distant reading...hegemony of cybernetic reason is everywhere today',\n",
        " u'Mercy Health shows how #BigData is like watching a million fireflies, and knowing each one. http://t.co/h8u4ugFz #healthcare #CEP #analytics',\n",
        " u'White Paper: Marketing With Big Data to Increase ROI http://t.co/pBMeSHSQ',\n",
        " u'The Science Behind Social Media, Natural Language and Big Data (Infographic.. http://t.co/LzEQuL67 (via @ArtilleryMarket)']"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we do some cleanup of the common words above, so that we can then compute some basic statistics:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "remove = tu.removal_set(words_to_remove, query)\n",
      "lines = tu.lines_cleanup([tweet['text'].encode('utf-8') for tweet in results], remove=remove)\n",
      "words = '\\n'.join(lines).split()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute frequency histogram:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "wf = tu.word_freq(words)\n",
      "sorted_wf = tu.sort_freqs(wf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at a summary of the word frequencies from this dataset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tu.summarize_freq_hist(sorted_wf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of unique words: 997\n",
        "\n",
        "10 least frequent words:\n",
        "           represent -> 1\n",
        "               chain -> 1\n",
        "http://t.co/jbc0dqzg -> 1\n",
        "             managem -> 1\n",
        "               leads -> 1\n",
        "               talks -> 1\n",
        "                disk -> 1\n",
        "           reasoning -> 1\n",
        "   @artillerymarket) -> 1\n",
        "           retention -> 1\n",
        "\n",
        "10 most frequent words:\n",
        "    marketing -> 9\n",
        "   #analytics -> 13\n",
        "    analytics -> 14\n",
        "(infographic) -> 22\n",
        "      natural -> 29\n",
        "     language -> 29\n",
        "       behind -> 30\n",
        "       social -> 32\n",
        "      science -> 32\n",
        "        media -> 32\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can plot the histogram of the `n_words` most frequent words:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_words = 10\n",
      "tu.plot_word_histogram(sorted_wf, n_words,\"Frequencies for %s most frequent words\" % n_words);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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IiYnBe++9h8KFC2PAgAHGD7MXrRMvLy+MGjUKtra2+Ouvv566nuvXr4+wsDDjyPbxvwGg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\nfgdO0OV/hafUlgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ac59c10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Above we trimmed the historgram to only show `n_words` because the distribution is very sharply peaked; this is what the histogram for the whole word list looks like:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tu.plot_word_histogram(sorted_wf, 1.0, \"Frequencies for entire word list\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ggJ566ilf1oRmcLvdrV3CZY8+9j/6+OKgny8dLQ7nn/3sZ3rmmWf0+uuv6+mn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\nAADLEM4AAFiGcAYAwDL/D7/YHPt9TtM8AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a244990>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Co-occurrence graph"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An interesting question to ask is: which pairs of words co-occur in the same tweets?  We can find these relations and use them to construct a graph, which we can then analyze with NetworkX and plot with Matplotlib.\n",
      "\n",
      "We limit the graph to have at most `n_nodes` (for the most frequent words) just to keep the visualization easier to read."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_nodes = 10\n",
      "popular = sorted_wf[-n_nodes:]\n",
      "pop_words = [wc[0] for wc in popular]\n",
      "co_occur = tu.co_occurrences(lines, pop_words)\n",
      "wgraph = tu.co_occurrences_graph(popular, co_occur, cutoff=1)\n",
      "wgraph = nx.connected_component_subgraphs(wgraph)[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An interesting summary of the graph structure can be obtained by ranking nodes based on a centrality measure.  NetworkX offers several centrality measures, in this case we look at the [Eigenvector Centrality](http://networkx.lanl.gov/reference/generated/networkx.algorithms.centrality.eigenvector_centrality.html#networkx.algorithms.centrality.eigenvector_centrality):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centrality = nx.eigenvector_centrality_numpy(wgraph)\n",
      "tu.summarize_centrality(centrality)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Graph centrality\n",
        "         social: 0.389\n",
        "          media: 0.389\n",
        "        science: 0.383\n",
        "       language: 0.383\n",
        "         behind: 0.383\n",
        "        natural: 0.383\n",
        "  (infographic): 0.332\n",
        "      analytics: 0.00952\n",
        "     #analytics: 0.00952\n",
        "      marketing: 0.00712\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we can use this measure to provide an interesting view of the structure of our query dataset:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Graph visualization for query:\", query\n",
      "tu.plot_graph(wgraph, tu.centrality_layout(wgraph, centrality), plt.figure(figsize=(8,8)),\n",
      "    title='Centrality and term co-occurrence graph, q=\"%s\"' % query)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Graph visualization for query: big data\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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UOBFCCCGEmIgCJ0IIIYQQE1HgRAghhBBiIgqcCCGEEEJMRIETIYQQQoiJKHAi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xQeGmwOa8zVhzdDXe2PgG5i2ZC4Phxr+mrmffvn2ttu3du1ewvpnQaldfB1dXV0REtIQlDQYD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\nnJXjBIoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b2c2790>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}

This doesn't work any longer. Your twitter requests need to be authorized...

TwythonError: 'Bad Request: The request was invalid. An accompanying error message will explain why. This is the status code will be returned during rate limiting. -- An error occurred processing your request.'

https://pypi.python.org/pypi/twython

The link above describes the form but getting authorization was beyond me. If I figure it out I will post a real solution but for now i just hope this saves someone the time I wasted.

cool way of distributing ipython notebooks though!

I've updated the code so that now it's working, you just have to go to here and register an application to get a Consumer Key and a Consumer Secret.

I get a long error while performing the query which is given below:

---

TwythonAuthError Traceback (most recent call last)
in ()
4 retweets = []
5 for page in range(1, n_pages+1):
----> 6 search = twitter.search(q=query+' lang:en', page=str(page))
7 res = search['results']
8 if not res:

/usr/local/lib/python2.7/dist-packages/twython/endpoints.pyc in search(self, **params)
134
135 """
--> 136 return self.get('search/tweets', params=params)
137 search.iter_mode = 'id'
138 search.iter_key = 'statuses'

/usr/local/lib/python2.7/dist-packages/twython/api.pyc in get(self, endpoint, params, version)
228 def get(self, endpoint, params=None, version='1.1'):
229 """Shortcut for GET requests via :class:request"""
--> 230 return self.request(endpoint, params=params, version=version)
231
232 def post(self, endpoint, params=None, version='1.1'):

/usr/local/lib/python2.7/dist-packages/twython/api.pyc in request(self, endpoint, method, params, version)
222 url = '%s/%s.json' % (self.api_url % version, endpoint)
223
--> 224 content = self._request(url, method=method, params=params, api_call=url)
225
226 return content

/usr/local/lib/python2.7/dist-packages/twython/api.pyc in _request(self, url, method, params, api_call)
192 raise ExceptionType(error_message,
193 error_code=response.status_code,
--> 194 retry_after=response.headers.get('retry-after'))
195
196 # if we have a json error here, then it's not an official Twitter API error

TwythonAuthError: Twitter API returned a 400 (Bad Request), Bad Authentication data

Hi
I tried to run
wgraph = nx.connected_component_subgraphs(wgraph)[0]
but I received

TypeError Traceback (most recent call last)
in ()
5 co_occur = co_occurrences(twt_sent, pop_words)
6 wgraph = co_occurrences_graph(popular, co_occur, cutoff=1)
----> 7 wgraph = nx.connected_component_subgraphs(wgraph)[0]

TypeError: 'generator' object is not subscriptable