hist3368-week11-word-embeddings-over-time-in-reddit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For citation information, please see the \"Source Information\" section listed in the associated README file: https://github.com/stephbuon/digital-history/tree/master/hist3368-week12-word-context-vectors"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hist 3368 - Word Context Vectors for Reddit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Jo Guldi and Ryan Schaefer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we'll learn some more basics of working with Python. We'll also use our newfound skills at \"running\" code in Jupyter to create some very intensive visualizations of how word usage has changed over time on Reddit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basics: loading software from elsewhere"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing new software packages\n",
    "\n",
    "Users of code often borrow software written by other people because it means taking a shortcut rather than reinventing the wheel.\n",
    "\n",
    "We will frequently 'import' software packages so that we can use commands and variables that other people have invented.\n",
    "\n",
    "In general, we will import software packages with the command 'import.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import wordnet as wn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the line of code above. If all goes well, you won't get an error message.  You'll just see a star (*) by the cell while the computer thinks for a moment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need to 'import' a software package at the beginning of each session in which you use that package.  Most of our future notebooks will begin with a string of 'import' commands to set things up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installing new software packages\n",
    "\n",
    "The first time you use a new software package, that package first needs to be 'installed' on your part of the super computer.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To install a new software package, we need special punctuation that tells M2 that we want control over the settings of the supercomputer.  Here is the syntax to install a package called 'bs4.'  We used many commands of this kind in the first-time-setup notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gensim in /users/rdschaefer/.local/lib/python3.7/site-packages (4.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.0 in /hpc/applications/intel/2020/intelpython3/lib/python3.7/site-packages (from gensim) (1.17.4)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /users/rdschaefer/.local/lib/python3.7/site-packages (from gensim) (6.2.0)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /hpc/applications/intel/2020/intelpython3/lib/python3.7/site-packages (from gensim) (1.3.2)\n"
     ]
    }
   ],
   "source": [
    "!/hpc/applications/intel/2020/intelpython3/bin/pip install gensim --user # you only need to run this once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim.models import KeyedVectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If any of the above 'import' commands failed to work, you can use the **!pip install xxx --user** syntax to install them  now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Error messages that indicate installations/importing are needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will also get error messages if you try to use a command from a package that hasn't been installed or imported.  \n",
    "\n",
    "For example, here is a function that I made up:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'breakdance' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-8b792f9f8fea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbreakdance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'breakdance' is not defined"
     ]
    }
   ],
   "source": [
    "breakdance(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If breakdance were a legitimate function, seeing the 'NameError' next to a function would be a good sign that we need to try installing and importing a software package that defines the function 'breakdance().'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working With Word Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we're going to be calling some files called \"word embeddings\" that were generated by your professor and stored on M2. Word embeddings are multi-dimensional models of language over time.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading pre-generated word embeddings models\n",
    "\n",
    "The following line of code tells the computer to look in the course folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/group/history/hist_3368-jguldi/reddit-embeddings\n"
     ]
    }
   ],
   "source": [
    "cd /scratch/group/history/hist_3368-jguldi/reddit-embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we tell the computer to load the pre-generated files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KeyedVectors.load(\"lemmatized-stopworded-bigrammed-reddit-model-10-full\", mmap='r')\n",
    "wv = model.wv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calling the model to investigate words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the word embeddings model loaded, we can use the commands 'wv(word)' (word vector) and 'wv.similar_by_vector(word, n)' to call the words are 'similar' in terms of the model's understanding of speech.\n",
    "\n",
    "With  'wv(word)' and 'wv.similar_by_vector(word_vector, n)', the user can switch out the \"word\" for any word spoken in English.  You can switch out n for any number.  That number of words will be returned.\n",
    "\n",
    "Note that the output is a list of words that were commonly used alongside the word \"man.\"  Each word is paired with a number, which represents ths \"similarity score\" of how frequently that word or phrase appears with the word \"man.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('man', 1.0000001192092896),\n",
       " ('old_man', 0.6203731298446655),\n",
       " ('guy', 0.5945892333984375),\n",
       " ('young_man', 0.5856431722640991),\n",
       " ('men', 0.584114134311676),\n",
       " ('son', 0.5592309236526489),\n",
       " ('little_girl', 0.541416585445404),\n",
       " ('lady', 0.5297858715057373),\n",
       " ('dude', 0.5285056233406067),\n",
       " ('homeless_man', 0.5157629251480103),\n",
       " ('somebody', 0.508521556854248),\n",
       " ('sinner', 0.5020511150360107),\n",
       " ('boy', 0.4995115399360657),\n",
       " ('poor_girl', 0.4987386167049408),\n",
       " ('old_men', 0.49842312932014465),\n",
       " ('wise_man', 0.49262696504592896),\n",
       " ('girl', 0.4910820424556732),\n",
       " ('fellow_soldier', 0.48736414313316345),\n",
       " ('bradley', 0.4871712028980255),\n",
       " ('julian', 0.48413416743278503),\n",
       " ('husband', 0.4831185042858124),\n",
       " ('daughter', 0.4775465428829193),\n",
       " ('woman', 0.4767850935459137),\n",
       " ('person', 0.4762783944606781),\n",
       " ('innocent_man', 0.4711743891239166)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man_vector = wv['man']\n",
    "wv.similar_by_vector(man_vector, 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the Contents of Your Vector Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note that in this exercise, you are less interested in learning every single command than you are in \"playing\" with the model to understand what it is telling you about speech on Reddit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the CONTEXT for One Word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('woman', 0.9999998807907104),\n",
       " ('muslim_men', 0.7996638417243958),\n",
       " ('men', 0.7837041020393372),\n",
       " ('young_woman', 0.760561466217041),\n",
       " ('muslim_woman', 0.7534667253494263),\n",
       " ('female', 0.7414042949676514),\n",
       " ('adult_woman', 0.7285311222076416),\n",
       " ('chattel', 0.7007821202278137),\n",
       " ('wear_burka', 0.696619987487793),\n",
       " ('wear_hijab', 0.6942851543426514)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woman_vector = wv['woman']\n",
    "wv.similar_by_vector(woman_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('person', 1.0),\n",
       " ('single_person', 0.6614776849746704),\n",
       " ('person_b', 0.6375599503517151),\n",
       " ('individual', 0.6372944116592407),\n",
       " ('somebody', 0.5750836133956909),\n",
       " ('normal_person', 0.5579050183296204),\n",
       " ('people', 0.5525158047676086),\n",
       " ('circumstance', 0.5466984510421753),\n",
       " ('justly', 0.5247067213058472),\n",
       " ('reasonable_person', 0.5124263167381287)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "individual_vector = wv['person']\n",
    "wv.similar_by_vector(individual_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('soldier', 0.9999999403953552),\n",
       " ('serviceman', 0.8503544926643372),\n",
       " ('american_soldier', 0.8469553589820862),\n",
       " ('solder', 0.834997832775116),\n",
       " ('fellow_soldier', 0.7874783277511597),\n",
       " ('single_soldier', 0.7804341316223145),\n",
       " ('american_troops', 0.7716955542564392),\n",
       " ('professional_soldier', 0.770550012588501),\n",
       " ('military_personnel', 0.7604196667671204),\n",
       " ('marines', 0.757709264755249)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soldier_vector = wv['soldier']\n",
    "wv.similar_by_vector(soldier_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interpreting vector similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try your own hand at interpreting these outputs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do you interpret these similarities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('usa', 0.765667200088501),\n",
       " ('country', 0.7602417469024658),\n",
       " ('nation', 0.6978814601898193),\n",
       " ('american', 0.6845840215682983),\n",
       " ('western_world', 0.6207195520401001),\n",
       " ('europe', 0.6099562644958496),\n",
       " ('western_nation', 0.6044434309005737),\n",
       " ('world', 0.5820668339729309),\n",
       " ('entire_world', 0.5687674283981323),\n",
       " ('western_democracy', 0.5667492151260376),\n",
       " ('usa_usa', 0.5611153244972229),\n",
       " ('latin_america', 0.5549983382225037),\n",
       " ('prosperous', 0.549216628074646),\n",
       " ('africa_south', 0.5437699556350708),\n",
       " ('britain', 0.5390774011611938),\n",
       " ('abroad', 0.5390337109565735),\n",
       " ('western_civilization', 0.5371620655059814),\n",
       " ('powerful_nation', 0.5300259590148926),\n",
       " ('melting_pot', 0.5273915529251099),\n",
       " ('western_country', 0.5269902944564819)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.most_similar(\"america\", topn = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('great_britain', 0.8194035887718201),\n",
       " ('england', 0.8172045946121216),\n",
       " ('france', 0.8023198246955872),\n",
       " ('argentina', 0.7774004936218262),\n",
       " ('germany_france', 0.773977518081665),\n",
       " ('uk', 0.7737563848495483),\n",
       " ('england_france', 0.7659919857978821),\n",
       " ('british', 0.7605403661727905),\n",
       " ('germany', 0.7558653950691223),\n",
       " ('italy', 0.7479751706123352),\n",
       " ('british_french', 0.744282066822052),\n",
       " ('europe', 0.7352380156517029),\n",
       " ('britain_france', 0.7339558601379395),\n",
       " ('spain', 0.7281656861305237),\n",
       " ('british_empire', 0.7266865968704224),\n",
       " ('england_germany', 0.7240669131278992),\n",
       " ('british_colony', 0.7231487035751343),\n",
       " ('uk_france', 0.7215187549591064),\n",
       " ('germany_italy', 0.7209317684173584),\n",
       " ('france_germany', 0.718067467212677)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.most_similar(\"britain\", topn = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subtracting Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('fucking_tool', 0.33393731713294983),\n",
       " ('titanic', 0.33050981163978577),\n",
       " ('cosmic', 0.3266211152076721),\n",
       " ('bleed_dry', 0.3264284133911133),\n",
       " ('holy_fuck', 0.32639673352241516),\n",
       " ('slow_clap', 0.3262399137020111),\n",
       " ('moon_base', 0.31349900364875793),\n",
       " ('grand_plan', 0.3098754286766052),\n",
       " ('vulcan', 0.30901798605918884),\n",
       " ('uncovering', 0.30600598454475403)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = wv['man'] - wv['woman']\n",
    "wv.similar_by_vector(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('woman', 0.7043781876564026),\n",
       " ('muslim_woman', 0.6014901995658875),\n",
       " ('subjugation', 0.5858792662620544),\n",
       " ('wear_burka', 0.585241973400116),\n",
       " ('wear_burqa', 0.5688230395317078),\n",
       " ('reproductive', 0.5624473690986633),\n",
       " ('suffrage', 0.5455923080444336),\n",
       " ('chattel', 0.5431570410728455),\n",
       " ('sexism', 0.5352342128753662),\n",
       " ('wearing_burqa', 0.5245528221130371)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = wv['woman'] - wv['boy']\n",
    "wv.similar_by_vector(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('main_reason', 0.431571900844574),\n",
       " ('sad_thing', 0.4269271194934845),\n",
       " ('young_people', 0.4243943989276886),\n",
       " ('complacent', 0.42340144515037537),\n",
       " ('war_machine', 0.41575855016708374),\n",
       " ('building_infrastructure', 0.4004659950733185),\n",
       " ('folks', 0.399309903383255),\n",
       " ('real_reason', 0.39374813437461853),\n",
       " ('american', 0.38561248779296875),\n",
       " ('pretty_soon', 0.3846699297428131)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = wv['people'] - wv['person']\n",
    "wv.similar_by_vector(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('reasonable_attempt', 0.615293025970459),\n",
       " ('person_b', 0.6128267645835876),\n",
       " ('person', 0.5853927135467529),\n",
       " ('practicable', 0.5642107725143433),\n",
       " ('affirmative_defense', 0.5389994978904724),\n",
       " ('defendant', 0.5202764868736267),\n",
       " ('reasonable_suspicion', 0.5201581716537476),\n",
       " ('unlawful_coercion', 0.5199625492095947),\n",
       " ('assault_battery', 0.5199410319328308),\n",
       " ('sexual_intercourse', 0.5187230110168457)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = wv['person'] - wv['people']\n",
    "wv.similar_by_vector(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('think', 0.5499918460845947),\n",
       " ('m_saying', 0.5026589632034302),\n",
       " ('m_sure', 0.48010459542274475),\n",
       " ('obviously', 0.45594522356987),\n",
       " ('agree', 0.4480757713317871),\n",
       " ('assume', 0.4401354491710663),\n",
       " ('argue', 0.4278944432735443),\n",
       " ('exactly', 0.4252399504184723),\n",
       " ('classify', 0.4234572947025299),\n",
       " ('actually', 0.4183546304702759)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = wv['think'] - wv['heart']\n",
    "wv.similar_by_vector(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('feel', 0.7242302298545837),\n",
       " ('feeling', 0.5987159609794617),\n",
       " ('felt', 0.5765516757965088),\n",
       " ('pang', 0.5312406420707703),\n",
       " ('fellow_humans', 0.4894363582134247),\n",
       " ('sorrow', 0.4834098815917969),\n",
       " ('anguish', 0.4814719557762146),\n",
       " ('sadness', 0.4782954454421997),\n",
       " ('feelings', 0.4720255136489868),\n",
       " ('fellow_countryman', 0.4701686501502991)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = wv['feel'] - wv['think']\n",
    "wv.similar_by_vector(diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding vectors to find synonyms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('muslim_men', 0.8710395693778992),\n",
       " ('woman', 0.8660286068916321),\n",
       " ('adult_woman', 0.8550558686256409),\n",
       " ('gay_men', 0.8431201577186584),\n",
       " ('promiscuous', 0.8391146063804626),\n",
       " ('female', 0.8261219263076782),\n",
       " ('straight_men', 0.8248839378356934),\n",
       " ('older_men', 0.8243266940116882),\n",
       " ('gay_male', 0.8165796995162964),\n",
       " ('sexually', 0.8160114884376526)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_context = [word[0] for word in wv.most_similar(\"woman\", topn = 100)]\n",
    "\n",
    "sum = wv[keyword_context[0]] \n",
    "\n",
    "for word in keyword_context[1:len(keyword_context)]:\n",
    "    next_vector = wv[word] \n",
    "    sum = sum + next_vector\n",
    "    \n",
    "wv.similar_by_vector(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('soldier', 0.9124206304550171),\n",
       " ('solder', 0.8865453600883484),\n",
       " ('combat_zone', 0.8521919250488281),\n",
       " ('single_soldier', 0.8471569418907166),\n",
       " ('serviceman', 0.8461081385612488),\n",
       " ('civilian', 0.8416454195976257),\n",
       " ('enemy_soldier', 0.8398593664169312),\n",
       " ('american_soldier', 0.8392512202262878),\n",
       " ('american_troops', 0.8362427353858948),\n",
       " ('insurgent', 0.8354545831680298)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_context = [word[0] for word in wv.most_similar(\"soldier\", topn = 100)]\n",
    "sum = wv[keyword_context[0]] \n",
    "for word in keyword_context[1:len(keyword_context)]:\n",
    "    next_vector = wv[word] \n",
    "    sum = sum + next_vector\n",
    "wv.similar_by_vector(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('happy', 0.8531821966171265),\n",
       " ('grateful', 0.677687406539917),\n",
       " ('comfortable_life', 0.6667778491973877),\n",
       " ('miserable', 0.6576833128929138),\n",
       " ('decent_life', 0.650288999080658),\n",
       " ('lucky', 0.6420705318450928),\n",
       " ('comfortable', 0.64084792137146),\n",
       " ('work_hard', 0.6384710669517517),\n",
       " ('bust_ass', 0.6347267031669617),\n",
       " ('thankful', 0.6346752047538757)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_context = [word[0] for word in wv.most_similar(\"happy\", topn = 100)]\n",
    "sum = wv[keyword_context[0]] \n",
    "for word in keyword_context[1:len(keyword_context)]:\n",
    "    next_vector = wv[word] \n",
    "    sum = sum + next_vector\n",
    "wv.similar_by_vector(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('american', 0.8358689546585083),\n",
       " ('foreigner', 0.7439253330230713),\n",
       " ('abroad', 0.7400301694869995),\n",
       " ('country', 0.7107583284378052),\n",
       " ('america', 0.6918519735336304),\n",
       " ('foreign_country', 0.6917643547058105),\n",
       " ('usa', 0.6861141324043274),\n",
       " ('latin_american', 0.678594708442688),\n",
       " ('iraqi_afghan', 0.6695930361747742),\n",
       " ('european', 0.6609619855880737)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_context = [word[0] for word in wv.most_similar(\"american\", topn = 100)]\n",
    "sum = wv[keyword_context[0]] \n",
    "for word in keyword_context[1:len(keyword_context)]:\n",
    "    next_vector = wv[word] \n",
    "    sum = sum + next_vector\n",
    "wv.similar_by_vector(sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distance and Similarity with Vectors in GENSIM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With similarity, the higher the number, the more alike two terms are in the context in which they are used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7414043"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.similarity('woman', 'female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47678512"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.similarity('woman', 'man')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29918987"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.similarity('soldier', 'man')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32235694"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.similarity('woman', 'person')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09377818"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv.similarity('woman', 'rock')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the similarities as a Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords = ['dream',  'war',  'wealth', 'happy',  'tomorrow', 'past', 'present', 'future', 'america', 'democracy', 'riot', 'dictator', 'money', 'oppression', 'prison',  'britain', 'china', 'democrat', 'republican', 'welfare', 'congress', 'protest']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword_vectors = wv[keywords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "links = linkage(keyword_vectors, method='complete', metric='seuclidean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "l = links\n",
    "\n",
    "# calculate full dendrogram\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.ylabel('word')\n",
    "plt.xlabel('distance')\n",
    "\n",
    "dendrogram(\n",
    "    l,\n",
    "    leaf_rotation=0,  # rotates the x axis labels\n",
    "    leaf_font_size=16,  # font size for the x axis labels\n",
    "    orientation='left',\n",
    "    leaf_label_func=lambda v: str(keywords[v])\n",
    ")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: if you get an error above, delete any words from the list.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Abstract Relatedness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from gensim.test.utils import datapath, get_tmpfile\n",
    "from gensim.models import KeyedVectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "\n",
    "def display_pca_scatterplot(model, words=None, sample=0):\n",
    "    if words == None:\n",
    "        if sample > 0:\n",
    "            words = np.random.choice(list(model.wv.key_to_index.keys()), sample)\n",
    "        else:\n",
    "            words = [ word for word in model.wv.key_to_index ]\n",
    "        \n",
    "    word_vectors = np.array([model[w] for w in words])\n",
    "\n",
    "    twodim = PCA().fit_transform(word_vectors)[:,:2]\n",
    "    \n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n",
    "    for word, (x,y) in zip(words, twodim):\n",
    "        plt.text(x+0.05, y+0.05, word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_pca_scatterplot(wv, keywords)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Study change over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataname = 'lemmatized-stopworded-bigrammed-reddit-model-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyword1 = 'war'  # the word you want to research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['07-full', '08-full', '09-full', '10-full', '11-full']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periodnames = []\n",
    "years = [\"07\", \"08\", \"09\", \"10\", \"11\"]\n",
    "for i in years:\n",
    "    periodnames.append(i + \"-full\") \n",
    "periodnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on  07-full\n",
      "found  war\n",
      "working on  08-full\n",
      "found  war\n",
      "working on  09-full\n",
      "found  war\n",
      "working on  10-full\n",
      "found  war\n",
      "working on  11-full\n",
      "found  war\n"
     ]
    }
   ],
   "source": [
    "#########  after the first run, use this line to call the old data without generating it again\n",
    "keyword_context = []\n",
    "dates_found = []\n",
    "\n",
    "# cycle through each period\n",
    "for period1 in periodnames:\n",
    "    print('working on ', period1)\n",
    "    \n",
    "    # load the model from period1\n",
    "#     period_model = gensim.models.Word2Vec.load(dataname + str(period1) + '.0') # to load a saved model\n",
    "    period_wv = KeyedVectors.load(dataname + period1) # load the saved model\n",
    "    \n",
    "    ## is the keyword found?\n",
    "    if keyword1 in period_wv.wv.key_to_index:\n",
    "        print('found ', keyword1)\n",
    "        \n",
    "        # get the context vector for keyword1\n",
    "        keyword_context_period = period_wv.wv.most_similar(keyword1, topn = 5000) \n",
    "        \n",
    "        # save it for later\n",
    "        keyword_context.append(keyword_context_period) # save the context of how women were talked about for later\n",
    "        dates_found.append(period1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function to abstract only unique values while keeping the list in the same order -- the order of first appearance\n",
    "def unique2(seq):\n",
    "    seen = set()\n",
    "    seen_add = seen.add\n",
    "    return [x for x in seq if not (x in seen or seen_add(x))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words = []\n",
    "for i in range(len(dates_found)):\n",
    "    words = [item[0] for item in keyword_context[i]][:10]\n",
    "    all_words.append(words)\n",
    "\n",
    "all_words2 = []\n",
    "for list in all_words:\n",
    "    for word in list:\n",
    "        all_words2.append(word)\n",
    "\n",
    "numwords = 10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/users/rdschaefer\n"
     ]
    }
   ],
   "source": [
    "cd ~/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6000x6000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "\n",
    "from adjustText import adjust_text\n",
    "from numpy import linspace\n",
    "from matplotlib import cm\n",
    "\n",
    "colors = [ cm.viridis(x) for x in linspace(0, 1, len(unique2(all_words2))+10) ]\n",
    "\n",
    "# change the figure's size here\n",
    "plt.figure(figsize=(20,20), dpi = 300)\n",
    "\n",
    "texts = []\n",
    "\n",
    "# plt.annotate only plots one label per iteration, so we have to use a for loop \n",
    "for i in range(len(dates_found)):    # cycle through the period names\n",
    "    \n",
    "    #yyy = int(keyword_per_year[keyword_per_year['5yrperiod'] == int(xx)]['count'])   # how many times was the keyword used that year?\n",
    "                     \n",
    "    for j in range(5):     # cycle through the first ten words (you can change this variable)\n",
    "        \n",
    "        xx = dates_found[i]        # on the x axis, plot the period name\n",
    "        yy = [item[1] for item in keyword_context[i]][j]         # on the y axis, plot the distance -- how closely the word is related to the keyword\n",
    "        txt = [item[0] for item in keyword_context[i]][j]        # grab the name of each collocated word\n",
    "        colorindex = unique2(all_words2).index(txt)   # this command keeps all dots for the same word the same color\n",
    "        \n",
    "        plt.scatter(                                             # plot dots\n",
    "            xx, #x axis\n",
    "            yy, # y axis\n",
    "            linewidth=1, \n",
    "            color = colors[colorindex],\n",
    "            edgecolors = 'darkgray',\n",
    "            s = 100, # dot size\n",
    "            alpha=0.8)  # dot transparency\n",
    "\n",
    "        # make a label for each word\n",
    "        texts.append(plt.text(xx, yy, txt))\n",
    "\n",
    "# Code to help with overlapping labels -- may take a minute to run\n",
    "adjust_text(texts, force_points=0.2, force_text=.7, \n",
    "                    expand_points=(1, 1), expand_text=(1, 1),\n",
    "                    arrowprops=dict(arrowstyle=\"-\", color='black', lw=0.5))\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Add titles\n",
    "plt.title(\"What words were most similar to ''\" + keyword1 + \"' on Reddit?\", fontsize=20, fontweight=0, color='Red')\n",
    "plt.xlabel(\"period\")\n",
    "plt.ylabel(\"similarity to \" + keyword1)\n",
    "\n",
    "\n",
    "filename = 'words-similar-to-' + keyword1 + '-' + dataname\n",
    "plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10 (main, Feb 25 2022, 18:07:24) \n[Clang 13.0.0 (clang-1300.0.29.30)]"
  },
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   "interpreter": {
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}