code/4-Social-networks.ipynb

{
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   "source": [
    "![UKDS Logo](images/UKDS_Logos_Col_Grey_300dpi.png)"
   ]
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  {
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   "metadata": {
    "slideshow": {
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   "source": [
    "# Text-mining: Named Entity Extraction and Social Network Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to this <a href=\"https://ukdataservice.ac.uk/\" target=_blank>UK Data Service</a> *Computational Social Science* training series! \n",
    "\n",
    "The various *Computational Social Science* training series, all of which guide you through some of the popular and useful computational techniques, tools, methods and concepts that social science research might want to use. For example, this series covers collecting data from websites and social media platorms, working with text data, conducting simulations (agent based modelling), and more. The series includes recorded video webinars, interactive notebooks containing live programming code, reading lists and more.\n",
    "\n",
    "* To access training materials on our GitHub site: <a href=\"https://github.com/UKDataServiceOpen/computational-social-science\" target=_blank>[Training Materials]</a>\n",
    "\n",
    "* To keep up to date with upcoming and past training events: <a href=\"https://ukdataservice.ac.uk/news-and-events/events\" target=_blank>[Events]</a>\n",
    "\n",
    "* To get in contact with feedback, ideas or to seek assistance: <a href=\"https://ukdataservice.ac.uk/help.aspx\" target=_blank>[Help]</a>\n",
    "\n",
    "<a href=\"https://www.research.manchester.ac.uk/portal/julia.kasmire.html\" target=_blank>Dr J. Kasmire</a>  <br />\n",
    "UK Data Service  <br />\n",
    "University of Manchester <br />"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "toc": true
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Acquire-and-prepare-a-set-of-documents-with-named-entities.\" data-toc-modified-id=\"Acquire-and-prepare-a-set-of-documents-with-named-entities.-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Acquire and prepare a set of documents with named entities.</a></span></li><li><span><a href=\"#Extract-the-desired-chunks\" data-toc-modified-id=\"Extract-the-desired-chunks-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Extract the desired chunks</a></span></li><li><span><a href=\"#Identify-which-extracted-proper-nouns-are-named-in-the-same-document\" data-toc-modified-id=\"Identify-which-extracted-proper-nouns-are-named-in-the-same-document-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Identify which extracted proper nouns are named in the same document</a></span></li><li><span><a href=\"#Create-a-network-graph-and-add-the-nodes\" data-toc-modified-id=\"Create-a-network-graph-and-add-the-nodes-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Create a network graph and add the nodes</a></span></li><li><span><a href=\"#Add-edges-to-the-network-graph\" data-toc-modified-id=\"Add-edges-to-the-network-graph-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Add edges to the network graph</a></span></li><li><span><a href=\"#Have-a-look-at-graph-info\" data-toc-modified-id=\"Have-a-look-at-graph-info-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>Have a look at graph info</a></span></li><li><span><a href=\"#Draw-the-graph\" data-toc-modified-id=\"Draw-the-graph-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>Draw the graph</a></span></li><li><span><a href=\"#Conclusions\" data-toc-modified-id=\"Conclusions-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>Conclusions</a></span></li><li><span><a href=\"#Further-reading\" data-toc-modified-id=\"Further-reading-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>Further reading</a></span></li></ul></div>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "There is a table of contents provided here at the top of the notebook, but you can also access this menu at any point by clicking the Table of Contents button on the top toolbar (an icon with four horizontal bars, if unsure hover your mouse over the buttons). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already saw how to add part-of-speech tags and named entity labels to a corpus, but now we will explore a practical applications for those POS-tags and entity labels - extracted the names of people and creating a social network based on which people are mentioned in the same document. \n",
    "\n",
    "There are, of course, many other very useful and practical applications for POS-tags and/or entity labels. However, as always, readers should interpret this notebook as being a demonstration of a popular option rather than an exhaustive or comprehensive guide to all possibilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acquire and prepare a set of documents with named entities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As always, let's start by importing and downloading some useful packages. Many of these will be familiar to you if you have worked through the 'Basic Extraction' notebook, but there are always some new tools to explore. \n",
    "\n",
    "Run/Shift+Enter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
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     "text": [
      "[nltk_data] Downloading package punkt to /Users/loucap/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /Users/loucap/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package maxent_ne_chunker to\n",
      "[nltk_data]     /Users/loucap/nltk_data...\n",
      "[nltk_data]   Package maxent_ne_chunker is already up-to-date!\n",
      "[nltk_data] Downloading package words to /Users/loucap/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: networkx in /opt/anaconda3/lib/python3.8/site-packages (2.8.4)\r\n"
     ]
    }
   ],
   "source": [
    "import nltk                                          # get nltk \n",
    "from nltk import word_tokenize, pos_tag, ne_chunk    # import some of our old favourte functions\n",
    "from nltk import Tree                                # and import some new functions\n",
    "nltk.download('punkt')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('maxent_ne_chunker')\n",
    "nltk.download('words')\n",
    "\n",
    "from itertools import chain, tee\n",
    "from operator import itemgetter\n",
    "\n",
    "!pip install networkx\n",
    "import networkx as nx                                # Just notice this line for now... We will refer to it again later.\n",
    "from networkx.algorithms import community\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_data = ['Archibald walked through Manchester with Beryl.', \n",
    "               'Tariq saw Beryl when she was playing tennis.', \n",
    "               'Archibald shares a house with Beryl and Cerys.',\n",
    "               'Cerys works with both Tariq and Edith.', \n",
    "               'Edith drives past Archibald and Denise on her morning commute.',\n",
    "               'Fadwa listens to podcasts while running.', \n",
    "               'Guo-feng and Hita often drive to the Welsh coast at weekends.',\n",
    "               'Icarus shops at the same supermarket as Janyu and Edith.', \n",
    "               'Kelsey and Edith both used to live in London.',\n",
    "               'Laia and Icarus are on the same bowling team.', \n",
    "               'Archibald and Kelsey are both keen gardeners.',\n",
    "               'Laia and Montserrat are both Catalonians. '\n",
    "              ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Named Entity chunkers require data that is:\n",
    "* word tokenised and \n",
    "* POS-tagged. \n",
    "\n",
    "Named Entity chunkers return a list of nested trees. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Tree('S', [Tree('PERSON', [('Archibald', 'NNP')]), ('walked', 'VBD'), ('through', 'IN'), ('Manchester', 'NNP'), ('with', 'IN'), Tree('PERSON', [('Beryl', 'NNP')]), ('.', '.')]), Tree('S', [Tree('PERSON', [('Tariq', 'NNP')]), ('saw', 'VBD'), Tree('PERSON', [('Beryl', 'NNP')]), ('when', 'WRB'), ('she', 'PRP'), ('was', 'VBD'), ('playing', 'VBG'), ('tennis', 'NN'), ('.', '.')]), Tree('S', [Tree('GPE', [('Archibald', 'NNP')]), ('shares', 'NNS'), ('a', 'DT'), ('house', 'NN'), ('with', 'IN'), Tree('PERSON', [('Beryl', 'NNP')]), ('and', 'CC'), Tree('PERSON', [('Cerys', 'NNP')]), ('.', '.')]), Tree('S', [Tree('GPE', [('Cerys', 'NNP')]), ('works', 'VBZ'), ('with', 'IN'), ('both', 'DT'), Tree('PERSON', [('Tariq', 'NNP')]), ('and', 'CC'), Tree('PERSON', [('Edith', 'NNP')]), ('.', '.')]), Tree('S', [Tree('GPE', [('Edith', 'NNP')]), ('drives', 'NNS'), ('past', 'JJ'), Tree('ORGANIZATION', [('Archibald', 'NNP')]), ('and', 'CC'), Tree('PERSON', [('Denise', 'NNP')]), ('on', 'IN'), ('her', 'PRP$'), ('morning', 'NN'), ('commute', 'NN'), ('.', '.')]), Tree('S', [Tree('GPE', [('Fadwa', 'NNP')]), ('listens', 'VBZ'), ('to', 'TO'), ('podcasts', 'NNS'), ('while', 'IN'), ('running', 'VBG'), ('.', '.')]), Tree('S', [('Guo-feng', 'NNP'), ('and', 'CC'), Tree('PERSON', [('Hita', 'NNP')]), ('often', 'RB'), ('drive', 'VBP'), ('to', 'TO'), ('the', 'DT'), ('Welsh', 'NNP'), ('coast', 'NN'), ('at', 'IN'), ('weekends', 'NNS'), ('.', '.')]), Tree('S', [Tree('GPE', [('Icarus', 'NNP')]), ('shops', 'NNS'), ('at', 'IN'), ('the', 'DT'), ('same', 'JJ'), ('supermarket', 'NN'), ('as', 'IN'), Tree('PERSON', [('Janyu', 'NNP')]), ('and', 'CC'), Tree('PERSON', [('Edith', 'NNP')]), ('.', '.')]), Tree('S', [Tree('GPE', [('Kelsey', 'NNP')]), ('and', 'CC'), Tree('PERSON', [('Edith', 'NNP')]), ('both', 'DT'), ('used', 'VBN'), ('to', 'TO'), ('live', 'VB'), ('in', 'IN'), Tree('GPE', [('London', 'NNP')]), ('.', '.')]), Tree('S', [Tree('GPE', [('Laia', 'NNP')]), ('and', 'CC'), Tree('GPE', [('Icarus', 'NNP')]), ('are', 'VBP'), ('on', 'IN'), ('the', 'DT'), ('same', 'JJ'), ('bowling', 'NN'), ('team', 'NN'), ('.', '.')]), Tree('S', [Tree('GPE', [('Archibald', 'NNP')]), ('and', 'CC'), Tree('GPE', [('Kelsey', 'NNP')]), ('are', 'VBP'), ('both', 'DT'), ('keen', 'JJ'), ('gardeners', 'NNS'), ('.', '.')]), Tree('S', [Tree('GPE', [('Laia', 'NNP')]), ('and', 'CC'), ('Montserrat', 'NNP'), ('are', 'VBP'), ('both', 'DT'), ('Catalonians', 'NNP'), ('.', '.')])]\n"
     ]
    }
   ],
   "source": [
    "tagged_chunked_data = []\n",
    "for item in social_data:\n",
    "    tokens = word_tokenize(item)\n",
    "    tags = nltk.pos_tag(tokens)\n",
    "    chunks = ne_chunk(tags)\n",
    "    tagged_chunked_data.append(chunks)\n",
    "    \n",
    "print(tagged_chunked_data)                       # print everything, since this is a small enough list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the results, we can see that each item in the list is a tree because each sentence starts with a \"Tree('S')\" indicating that the highest level of tree is a Sentence Tree. \n",
    "\n",
    "But Sentence Trees can have sub-trees. Each sub-tree also starts with \"Tree('TREE_TYPE')\" and we can see that there are \"Tree('PERSON')\", \"Tree('ORGANISATION')\" and \"Tree('GPE')\". Unfortunately, the ne_chunker is not perfect. 'Edith' is listed as a 'GPE' in one place but a 'PERSON' in another. Likewise, 'Archibald' is both a 'PERSON' and an 'ORGANISATION', while 'Guo-feng' and 'Montserrat' are not identified as sub-trees at all. You can probably find more mis-classifications. \n",
    "\n",
    "Let's take a closer look at those sentences. Run/Shift+Enter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  (PERSON Archibald/NNP)\n",
      "  walked/VBD\n",
      "  through/IN\n",
      "  Manchester/NNP\n",
      "  with/IN\n",
      "  (PERSON Beryl/NNP)\n",
      "  ./.)\n"
     ]
    }
   ],
   "source": [
    "print(tagged_chunked_data[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to change the number in the above code block and re-run it to look at other sentences. \n",
    "\n",
    "Ultimately, a NER chunker is a classifier and can be trained on custom data as we have already seen how to do (check the classifier jupyter notebook in the same folder as this one!). \n",
    "\n",
    "Feel free to try training your own NER chunker classifier. You'll need to put social_data through a word_tokenisation process, then use its output to create a training data set. Then train a classifier on your training data set. \n",
    "\n",
    "But for now, we carry on!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract the desired chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have some reasonable chunks all chunked up nicely, we want to extract the desired chunks so that they can become the nodes in our network. \n",
    "\n",
    "Run/Shift+Enter!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Archibald', 'Beryl', 'Manchester'], ['Beryl', 'Tariq'], ['Archibald', 'Beryl', 'Cerys'], ['Cerys', 'Edith', 'Tariq'], ['Archibald', 'Denise', 'Edith'], ['Fadwa'], ['Guo-feng', 'Hita', 'Welsh'], ['Edith', 'Icarus', 'Janyu'], ['Edith', 'Kelsey', 'London'], ['Icarus', 'Laia'], ['Archibald', 'Kelsey'], ['Catalonians', 'Laia', 'Montserrat']]\n"
     ]
    }
   ],
   "source": [
    "extracted_persons = []\n",
    "for tagged_tree in tagged_chunked_data:\n",
    "    people = []\n",
    "    for leaf in tagged_tree.leaves():\n",
    "        if 'NNP' in leaf[1]:\n",
    "            people.append(leaf[0])        \n",
    "    extracted_persons.append(sorted(people))\n",
    "    \n",
    "print(extracted_persons)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the results of the code above. It is a list of lists. Each of the sub-lists contains all the nouns that occur in a given sentence. For example, the first sentence was 'Archibald walked through Manchester with Beryl.' and the first sub-list in our extracted_persons list contains 'Archibald', 'Beryl', 'Manchester'. Seems about right. \n",
    "\n",
    "Well, maybe not perfect. Perhaps we have been a bit too generous... Extracted_persons has accurately extracted all the peoples' names, but it has also extracted place names and proper noun categories too. 'Manchester', 'Welsh', 'London' and 'Catalonians' are extracted too. This is because the above code looks for chunks that have POS-tags indicating they are proper nouns (NNP) and extracts those, rather than strictly looking for the names of people. \n",
    "\n",
    "We could try to use the Named Entity Recognition labels, extracting only those labelled as 'PERSON'... But we saw that those are not working reliably for our data set. As an alternative to training our own NER chunker, we could just manually review the list and remove the place or category names.\n",
    "\n",
    "But... maybe we are happy to leave the place names and proper noun categories too. After all, we can infer a kind of relationship between  people and places or categories. When Archibald and Beryl go walking through Manchester, they have a relationship with Manchester. If someone else also has a relationship with Manchester, then it is not exactly wrong to suggest that the third person has a somewhat distant relationship with Archibald and Beryl by virtue of their shared link to Manchester. \n",
    "S\n",
    "So, as an exacutive decision, I am going to leave all the proper nouns in. They will all become nodes in our network. But to do that, we need to find all of the unique entries, which I find is helpful to view in alphabetical order. \n",
    "\n",
    "Run/Shift+Enter, duuuuuuuuuuuuude!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Archibald', 'Beryl', 'Catalonians', 'Cerys', 'Denise', 'Edith', 'Fadwa', 'Guo-feng', 'Hita', 'Icarus', 'Janyu', 'Kelsey', 'Laia', 'London', 'Manchester', 'Montserrat', 'Tariq', 'Welsh']\n"
     ]
    }
   ],
   "source": [
    "unique_people = sorted(list(set(chain(*extracted_persons))))\n",
    "print(unique_people)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identify which extracted proper nouns are named in the same document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the alphabetised list of all unique proper nouns will become the nodes list in when we go to create a network. But we also need an edge list telling the network with nodes are connected. In practical terms, an edge list is a list of tuples, each containing two nodes. \n",
    "\n",
    "To do this, we can use the itertools.permutations which looks a list and creates a new list of tuples with all possible permutations of a fixed length that can be made from the original list. To be specific, the code below looks at each of the sub-lists of extracted_persons and creates 2 item tuples from all possible combinations of the items in the sub-list. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Archibald', 'Beryl'), ('Archibald', 'Manchester'), ('Beryl', 'Archibald'), ('Beryl', 'Manchester'), ('Manchester', 'Archibald'), ('Manchester', 'Beryl'), ('Beryl', 'Tariq'), ('Tariq', 'Beryl'), ('Archibald', 'Beryl'), ('Archibald', 'Cerys'), ('Beryl', 'Archibald'), ('Beryl', 'Cerys'), ('Cerys', 'Archibald'), ('Cerys', 'Beryl'), ('Cerys', 'Edith'), ('Cerys', 'Tariq'), ('Edith', 'Cerys'), ('Edith', 'Tariq'), ('Tariq', 'Cerys'), ('Tariq', 'Edith'), ('Archibald', 'Denise'), ('Archibald', 'Edith'), ('Denise', 'Archibald'), ('Denise', 'Edith'), ('Edith', 'Archibald'), ('Edith', 'Denise'), ('Guo-feng', 'Hita'), ('Guo-feng', 'Welsh'), ('Hita', 'Guo-feng'), ('Hita', 'Welsh'), ('Welsh', 'Guo-feng'), ('Welsh', 'Hita'), ('Edith', 'Icarus'), ('Edith', 'Janyu'), ('Icarus', 'Edith'), ('Icarus', 'Janyu'), ('Janyu', 'Edith'), ('Janyu', 'Icarus'), ('Edith', 'Kelsey'), ('Edith', 'London'), ('Kelsey', 'Edith'), ('Kelsey', 'London'), ('London', 'Edith'), ('London', 'Kelsey'), ('Icarus', 'Laia'), ('Laia', 'Icarus'), ('Archibald', 'Kelsey'), ('Kelsey', 'Archibald'), ('Catalonians', 'Laia'), ('Catalonians', 'Montserrat'), ('Laia', 'Catalonians'), ('Laia', 'Montserrat'), ('Montserrat', 'Catalonians'), ('Montserrat', 'Laia')]\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "co_occurring_pairs = []\n",
    "for people in extracted_persons:\n",
    "        for each_permutation in itertools.permutations(people, 2):\n",
    "            co_occurring_pairs.append(each_permutation)\n",
    "\n",
    "print(co_occurring_pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look. \n",
    "\n",
    "The first sub-list in extracted_persons was:\n",
    "- 'Archibald', 'Beryl', 'Manchester'\n",
    "\n",
    "At the start of our co_occurring_pairs, we have:\n",
    "- ('Archibald', 'Beryl'),\n",
    "- ('Archibald', 'Manchester'),\n",
    "- ('Beryl', 'Archibald'),\n",
    "- ('Beryl', 'Manchester'),\n",
    "- ('Manchester', 'Archibald'),\n",
    "- ('Manchester', 'Beryl')\n",
    "\n",
    "This means that we have an edge between 'Archibald' and 'Beryl', but also another edge between 'Beryl' and 'Archibald'.\n",
    "\n",
    "There are also no edges involving 'Fadwa' as she only appears in one sentence that has no other proper nouns. Thus, we will have at least one node with no edges.  \n",
    "\n",
    "These two points may or may not be a problem for you, depending on how you want your network to function. For example, you may want some or all of your links to be directed, meaning that the link only goes one way. \n",
    "In our network, this might be reasonable for sentences like  'Tariq saw Beryl when she was playing tennis.' since we don't know that Beryl also saw Tariq. Directed links like this would be especially important for networks based on scientific citations or other links that are clearly one-way.\n",
    "\n",
    "Likewise, if you want your network to be weighted, you may want to add a third value to the tuples with how strong you want the link to be. When you go to create the network, you will need to sum up edges so that multiple instances of an edge between the same two nodes has a higher weight. \n",
    "\n",
    "To create a weighted edge list, run the code below. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Archibald', 'Beryl', 1), ('Archibald', 'Manchester', 1), ('Beryl', 'Archibald', 1), ('Beryl', 'Manchester', 1), ('Manchester', 'Archibald', 1), ('Manchester', 'Beryl', 1), ('Beryl', 'Tariq', 1), ('Tariq', 'Beryl', 1), ('Archibald', 'Beryl', 1), ('Archibald', 'Cerys', 1), ('Beryl', 'Archibald', 1), ('Beryl', 'Cerys', 1), ('Cerys', 'Archibald', 1), ('Cerys', 'Beryl', 1), ('Cerys', 'Edith', 1), ('Cerys', 'Tariq', 1), ('Edith', 'Cerys', 1), ('Edith', 'Tariq', 1), ('Tariq', 'Cerys', 1), ('Tariq', 'Edith', 1), ('Archibald', 'Denise', 1), ('Archibald', 'Edith', 1), ('Denise', 'Archibald', 1), ('Denise', 'Edith', 1), ('Edith', 'Archibald', 1), ('Edith', 'Denise', 1), ('Guo-feng', 'Hita', 1), ('Guo-feng', 'Welsh', 1), ('Hita', 'Guo-feng', 1), ('Hita', 'Welsh', 1), ('Welsh', 'Guo-feng', 1), ('Welsh', 'Hita', 1), ('Edith', 'Icarus', 1), ('Edith', 'Janyu', 1), ('Icarus', 'Edith', 1), ('Icarus', 'Janyu', 1), ('Janyu', 'Edith', 1), ('Janyu', 'Icarus', 1), ('Edith', 'Kelsey', 1), ('Edith', 'London', 1), ('Kelsey', 'Edith', 1), ('Kelsey', 'London', 1), ('London', 'Edith', 1), ('London', 'Kelsey', 1), ('Icarus', 'Laia', 1), ('Laia', 'Icarus', 1), ('Archibald', 'Kelsey', 1), ('Kelsey', 'Archibald', 1), ('Catalonians', 'Laia', 1), ('Catalonians', 'Montserrat', 1), ('Laia', 'Catalonians', 1), ('Laia', 'Montserrat', 1), ('Montserrat', 'Catalonians', 1), ('Montserrat', 'Laia', 1)]\n"
     ]
    }
   ],
   "source": [
    "co_occurring_pairs_weighted = []\n",
    "\n",
    "for pair in co_occurring_pairs:\n",
    "    x = pair[0], pair[1], 1\n",
    "    co_occurring_pairs_weighted.append(x)\n",
    "    \n",
    "print(co_occurring_pairs_weighted)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a network graph and add the nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we initialise an empty network graph object. The 'nx' part of 'nx.Graph()' relies on code at the start of the notebook that imported networkx as nx. If you prefer, you can replace 'nx' with 'networkx'. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_network = nx.Graph()                             # Initialize an empty networkx graph object called 'social_network'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need to start filling up our empty graph object with details like the nodes list. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_network.add_nodes_from(unique_people)            # Add nodes to social_network from our extracted 'unique_people' list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm. Nothing happened.\n",
    "\n",
    "Well, that is not true. Something did happen, but we have to call extra functions to see what happened. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView(('Archibald', 'Beryl', 'Catalonians', 'Cerys', 'Denise', 'Edith', 'Fadwa', 'Guo-feng', 'Hita', 'Icarus', 'Janyu', 'Kelsey', 'Laia', 'London', 'Manchester', 'Montserrat', 'Tariq', 'Welsh'))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_network.nodes                                    # Use a graph object functions to see the nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add edges to the network graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming that we don't want a directed or weighted graph, we can add edges quite simply with code that is almost identical to the code we used to add nodes. \n",
    "\n",
    "This time, there is another line of code that calls on a different, but obviously similar, function to look at the edges. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([('Archibald', 'Beryl'), ('Archibald', 'Manchester'), ('Archibald', 'Cerys'), ('Archibald', 'Denise'), ('Archibald', 'Edith'), ('Archibald', 'Kelsey'), ('Beryl', 'Manchester'), ('Beryl', 'Tariq'), ('Beryl', 'Cerys'), ('Catalonians', 'Laia'), ('Catalonians', 'Montserrat'), ('Cerys', 'Edith'), ('Cerys', 'Tariq'), ('Denise', 'Edith'), ('Edith', 'Tariq'), ('Edith', 'Icarus'), ('Edith', 'Janyu'), ('Edith', 'Kelsey'), ('Edith', 'London'), ('Guo-feng', 'Hita'), ('Guo-feng', 'Welsh'), ('Hita', 'Welsh'), ('Icarus', 'Janyu'), ('Icarus', 'Laia'), ('Kelsey', 'London'), ('Laia', 'Montserrat')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_network.add_edges_from(co_occurring_pairs) # Add edges to social_network from our co-occurrence tuples\n",
    "social_network.edges                                     # Another quick look, this time at the just-imported edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not only does this list all of the edges in a helpful way (alphabetically by the first node) it is clear that there is only one edge between any two pairs of nodes. \n",
    "\n",
    "There is an Archibald-Beryl edge, but no Beryl-Archibald edge. Good to know, eh?\n",
    "\n",
    "But what if we want a weighted graph?\n",
    "\n",
    "Well, we need to run a more complicated code that checks if an edge exists and then either creates it or adds weight to it, as appropriate. To keep our weighted and unweighted graphs separate, we will also create a new empty graph called social_network_weighted, add nodes, and then add edges by checking to see if one already exists first. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([('Archibald', 'Beryl'), ('Archibald', 'Manchester'), ('Archibald', 'Cerys'), ('Archibald', 'Denise'), ('Archibald', 'Edith'), ('Archibald', 'Kelsey'), ('Beryl', 'Manchester'), ('Beryl', 'Tariq'), ('Beryl', 'Cerys'), ('Catalonians', 'Laia'), ('Catalonians', 'Montserrat'), ('Cerys', 'Edith'), ('Cerys', 'Tariq'), ('Denise', 'Edith'), ('Edith', 'Tariq'), ('Edith', 'Icarus'), ('Edith', 'Janyu'), ('Edith', 'Kelsey'), ('Edith', 'London'), ('Guo-feng', 'Hita'), ('Guo-feng', 'Welsh'), ('Hita', 'Welsh'), ('Icarus', 'Janyu'), ('Icarus', 'Laia'), ('Kelsey', 'London'), ('Laia', 'Montserrat')])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "social_network_weighted = nx.Graph()\n",
    "social_network_weighted.add_nodes_from(unique_people) \n",
    "\n",
    "for edge_pair in co_occurring_pairs_weighted:\n",
    "    if social_network_weighted.has_edge(edge_pair[0], edge_pair[1]):\n",
    "            # we added this one before, just increase the weight by one\n",
    "            w = int(edge_pair[2])\n",
    "            social_network_weighted[edge_pair[0]][edge_pair[1]]['weight'] += edge_pair[2]\n",
    "    else:\n",
    "            # new edge. add with weight=1\n",
    "            social_network_weighted.add_edge(edge_pair[0], edge_pair[1], weight = edge_pair[2])\n",
    "            \n",
    "social_network_weighted.edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Have a look at graph info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, some basic info about our two graphs using the nx.info function. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 18 nodes and 26 edges\n",
      "...\n",
      "Graph with 18 nodes and 26 edges\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dm/468m4xf52c71yhldz5ck50pw0000gn/T/ipykernel_62261/2714797816.py:1: DeprecationWarning: info is deprecated and will be removed in version 3.0.\n",
      "\n",
      "  print(nx.info(social_network))                          # the nx.info prints some basics about social_network\n",
      "/var/folders/dm/468m4xf52c71yhldz5ck50pw0000gn/T/ipykernel_62261/2714797816.py:4: DeprecationWarning: info is deprecated and will be removed in version 3.0.\n",
      "\n",
      "  print(nx.info(social_network_weighted))\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(social_network))                          # the nx.info prints some basics about social_network\n",
    "print('...')                                            # nx.info doesn't have everything you might want...\n",
    "\n",
    "print(nx.info(social_network_weighted))      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, you may want some additional info that is not included in the basics. \n",
    "\n",
    "Run/Shift+Enter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network density: 0.16993464052287582\n",
      "Network density: 0.16993464052287582\n"
     ]
    }
   ],
   "source": [
    "print(\"Network density:\", nx.density(social_network))               # but extra info is easy to get.\n",
    "print(\"Network density:\", nx.density(social_network_weighted)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Draw the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we need different visualisations for our different graphs. The two code blocks below show various ways that you can change the graph visualisation (layout, colour, node size, etc. )\n",
    "\n",
    "Run/Shift+Ente in the next 2 blocks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "social_network_positions = nx.circular_layout(social_network)  # Define positions for nodes according to a circular layout\n",
    "\n",
    "nx.draw_networkx_nodes(social_network, social_network_positions, # draw nodes according to position, size\n",
    "                       node_size=700) \n",
    "nx.draw_networkx_edges(social_network, social_network_positions,  # draw edges according to position, line width                      \n",
    "                       width=2)      \n",
    "nx.draw_networkx_labels(social_network, social_network_positions,  # draw labels according to position, font choices\n",
    "                        font_size=10, font_family='sans-serif')\n",
    "plt.show()                                                        # show the network as drawn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10, 10\n",
    "\n",
    "weighted_pos = nx.kamada_kawai_layout(social_network_weighted)   # Define positions for force directed node layout\n",
    "\n",
    "elarge = [(u, v) for (u, v, d) in social_network_weighted.edges(data=True) if d['weight'] > 2] #define a 'heavy edge' style\n",
    "esmall = [(u, v) for (u, v, d) in social_network_weighted.edges(data=True) if d['weight'] <= 2] # and a 'light edge' style\n",
    "# nodes\n",
    "nx.draw_networkx_nodes(social_network_weighted, weighted_pos, node_size=400)                    # draw the nodes\n",
    "\n",
    "# edges\n",
    "nx.draw_networkx_edges(social_network_weighted, weighted_pos, edgelist=elarge,                 #draw the heavy edges\n",
    "                       width=3)\n",
    "nx.draw_networkx_edges(social_network_weighted, weighted_pos, edgelist=esmall,                 #draw the light edges   \n",
    "                       width=1, alpha=0.5, edge_color='b', style='dashed')\n",
    "\n",
    "# labels\n",
    "nx.draw_networkx_labels(social_network_weighted, weighted_pos, font_size=15, font_family='serif')\n",
    "\n",
    "plt.axis('off')\n",
    "figure = plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hopefully, this will give you some ideas about what to do with the NLP processes that you have put your corpus through. There is clear value there, related to which things occur together. Different kinds of processing might help you get directed graphs (although that would take some clever classification relating to subjects, objects, etc. ). \n",
    "\n",
    "Please do feel free to start back at the beginning, adding more sentences with the same names or even with new names. \n",
    "\n",
    "As before, these exercises and this sample code should highlight to you that you need to think about:\n",
    "- your research questions and what you want to show, explore or understand, \n",
    "- your data, texts, corpus, or other research materials to analyse etc. \n",
    "- how your processes are related to your reserch questions, and \n",
    "- how your processes and data can be made available and reproducible. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Books, tutorials, package recommendations, etc. for Python\n",
    "\n",
    "- Natural Language Processing with Python by Steven Bird, Ewan Klein and Edward Loper, http://www.nltk.org/book/\n",
    "- Foundations of Statistical Natural Language Processing by Christopher Manning and Hinrich Schütze, https://nlp.stanford.edu/fsnlp/promo/\n",
    "- Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition by Dan Jurafsky and James H. Martin, https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf\n",
    "- Deep Learning in Natural Language Processing by Li Deng, Yang Liu, https://lidengsite.wordpress.com/book-chapters/\n",
    "- Sentiment Analysis data sets https://blog.cambridgespark.com/50-free-machine-learning-datasets-sentiment-analysis-b9388f79c124\n",
    "\n",
    "NLTK options\n",
    "- nltk.corpus http://www.nltk.org/howto/corpus.html\n",
    "- Data Camp tutorial on sentiment analysis with nltk https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python\n",
    "- Vader sentiment analysis script available on github (nltk) https://www.nltk.org/_modules/nltk/sentiment/vader.html\n",
    "- TextBlob https://textblob.readthedocs.io/en/dev/\n",
    "- Flair, a NLP script available on github https://github.com/flairNLP/flair\n",
    "\n",
    "networkx\n",
    "- package details https://networkx.github.io/documentation/stable/index.html\n",
    "- info about drawing graphs, including links to dedicated graph visualisation software https://networkx.github.io/documentation/stable/reference/drawing.html\n",
    "- drawing examples and specific tutorials https://networkx.github.io/documentation/latest/auto_examples/index.html\n",
    "- All the graph measures you can ask for https://networkx.github.io/documentation/stable/reference/algorithms/index.html\n",
    "\n",
    "\n",
    "Books and package recommendations for R\n",
    "- Quanteda, an R package for text analysis https://quanteda.io/​\n",
    "- Text Mining with R, a free online book https://www.tidytextmining.com/​"
   ]
  }
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