Final_Documented-nlplot_visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "grave-cassette",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install python-Levenshtein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sticky-cricket",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ajrfl\\anaconda3\\envs\\PythonAdv\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning:\n",
      "\n",
      "The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import nlplot\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "elect-indiana",
   "metadata": {},
   "outputs": [
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       "      Review_ID  Rating Reviewer_Location  \\\n",
       "0     122944085       5     United States   \n",
       "1     290851203       5    United Kingdom   \n",
       "2     168087311       4            Canada   \n",
       "3     282417360       5          Pakistan   \n",
       "4     160953212       3             India   \n",
       "...         ...     ...               ...   \n",
       "3999  290217727       5         Australia   \n",
       "4000  174264188       1    United Kingdom   \n",
       "4001  638583788       5     United States   \n",
       "4002  226159775       5       Philippines   \n",
       "4003  143521522       4    United Kingdom   \n",
       "\n",
       "                                            Review_Text  \\\n",
       "0     Spotlessly clean and so organized considering ...   \n",
       "1     Pricey but worth it, unfortunately the big thu...   \n",
       "2     Disneyland is a magical place. Everyone, parti...   \n",
       "3     Disney land is a must when visiting Paris. The...   \n",
       "4     The HK Disneyland is definitely small, but it'...   \n",
       "...                                                 ...   \n",
       "3999  There's a reason why they call Disneyland  The...   \n",
       "4000  I visited Disneyland Paris with my family (tee...   \n",
       "4001  What a great day we had we did so much before ...   \n",
       "4002  It has been a childhood dream of mine to go to...   \n",
       "4003  Well my wife and I visted in October 2012 for ...   \n",
       "\n",
       "                     Branch  Year  Month  is_good_review  \\\n",
       "0     Disneyland_California  2011     10               1   \n",
       "1          Disneyland_Paris  2015      7               1   \n",
       "2     Disneyland_California  2013      7               1   \n",
       "3          Disneyland_Paris  2015      6               1   \n",
       "4       Disneyland_HongKong  2012      9               0   \n",
       "...                     ...   ...    ...             ...   \n",
       "3999  Disneyland_California  2015      7               1   \n",
       "4000       Disneyland_Paris  2013      8               0   \n",
       "4001  Disneyland_California  2018     12               1   \n",
       "4002  Disneyland_California  2013     10               1   \n",
       "4003       Disneyland_Paris  2012     10               1   \n",
       "\n",
       "                                           review_clean    neg  ...  word_yet  \\\n",
       "0     spotlessly clean organize consider thousand vi...  0.000  ...       0.0   \n",
       "1     pricey worth unfortunately big thunder mountai...  0.115  ...       0.0   \n",
       "2     disneyland magical place everyone particularly...  0.000  ...       0.0   \n",
       "3     disney land must visit paris frozen show best ...  0.028  ...       0.0   \n",
       "4     hk disneyland definitely small definitely fun ...  0.000  ...       0.0   \n",
       "...                                                 ...    ...  ...       ...   \n",
       "3999  there's reason call disneyland happy place ear...  0.042  ...       0.0   \n",
       "4000  visit disneyland paris family teenage daughter...  0.081  ...       0.0   \n",
       "4001  great day much great guide grandson amaze well...  0.000  ...       0.0   \n",
       "4002  childhood dream mine go disneyland whether you...  0.000  ...       0.0   \n",
       "4003  well wife visted october parcs real complaint ...  0.030  ...       0.0   \n",
       "\n",
       "      word_york  word_you  word_young  word_yr  word_yrs  word_yum  \\\n",
       "0           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
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       "...         ...       ...         ...      ...       ...       ...   \n",
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       "\n",
       "      word_yummy  word_zero  word_zone  \n",
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       "\n",
       "[4004 rows x 2150 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read source file\n",
    "reviews_df = pd.read_csv(\"Resources/all_reviews_data.csv\", encoding='ISO-8859-1')\n",
    "\n",
    "\n",
    "# count of movie plot summaries\n",
    "# len(corpus)\n",
    "reviews_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "oriental-edgar",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creates text variables with and without stopwords\n",
    "NPT = nlplot.NLPlot(reviews_df, target_col='Review_Text')\n",
    "npt = nlplot.NLPlot(reviews_df, target_col='review_clean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "angry-gibson",
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = npt.get_stopword(top_n=30, min_freq=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "executive-sixth",
   "metadata": {},
   "source": [
    "## ngram barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "unusual-mexican",
   "metadata": {},
   "outputs": [
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      "100%|████████████████████████████████████████████████████████████████████████████| 4004/4004 [00:00<00:00, 7552.72it/s]\n"
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{\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Frequently Co-occurring Words\"}, \"width\": 800, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4358ae63-b00c-4875-bf26-81cfefadcada');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#renders barplot showing frequently co-occuring words in data\n",
    "npt.bar_ngram(title='uni-gram', ngram=1, top_n=50, stopwords=stopwords)\n",
    "#selects ngram visualization to bi-gram(2 words) ngram argument allows for trigrams, etc. \n",
    "bgram = NPT.bar_ngram(title='Frequently Co-occurring Words', ngram=2, top_n=50, stopwords=stopwords)\n",
    "bgram.write_html(\"Resources/common_bigrams.html\")\n",
    "bgram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electronic-institute",
   "metadata": {},
   "source": [
    "## LDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "searching-croatia",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'PreparedData' object has no attribute 'write_html'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-575c381c9c69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mpyLDAvis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_notebook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mLDA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mldavis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_topics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpasses\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mLDA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Resources/LDA.html\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mLDA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'PreparedData' object has no attribute 'write_html'"
     ]
    }
   ],
   "source": [
    "#creates a LDAvis Plot to perform themat analysis (NOTE: must instal pyLDAvis first)\n",
    "import pyLDAvis\n",
    "pyLDAvis.enable_notebook()\n",
    "LDA = npt.ldavis(num_topics=5, passes=5, save=False)\n",
    "LDA.write_html(\"Resources/LDA.html\")\n",
    "LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "chemical-disclosure",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NLPlot' object has no attribute 'node_df'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-9f10668fd805>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msunburst\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'sunburst chart'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolorscale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\PythonAdv\\lib\\site-packages\\nlplot\\nlplot.py\u001b[0m in \u001b[0;36msunburst\u001b[1;34m(self, title, colorscale, color_col, color_continuous_scale, width, height, save)\u001b[0m\n\u001b[0;32m    676\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    677\u001b[0m         \u001b[1;31m# make copy of node dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m         \u001b[0m_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnode_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m         \u001b[1;31m# change community label to string (needed for plot)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NLPlot' object has no attribute 'node_df'"
     ]
    }
   ],
   "source": [
    "npt.sunburst(title='sunburst chart', colorscale=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "humanitarian-williams",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "southeast-finland",
   "metadata": {},
   "outputs": [
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordhist = npt.word_distribution(title='words distribution')\n",
    "wordhist.write_html(\"Resources/wordhistogram.html\")\n",
    "wordhist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agricultural-conservation",
   "metadata": {},
   "source": [
    "## Wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "electric-removal",
   "metadata": {},
   "outputs": [],
   "source": [
    "#renders a simple, clean, and colorful wordcloud\n",
    "nptcloud = npt.wordcloud(stopwords=stopwords, colormap='tab20_r')\n",
    "nptcloud.write_html(\"Resources/nptcloud.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rolled-addiction",
   "metadata": {},
   "source": [
    "## Treemap without stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "respective-budget",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 4004/4004 [00:00<00:00, 19813.70it/s]\n"
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    },
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     "data": {
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          "y": [
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         },
         "hovertemplate": "labels=%{label}<br>word_count=%{value}<br>parent=%{parent}<br>id=%{id}<extra></extra>",
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          "around",
          "attraction",
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          "character",
          "child",
          "close",
          "come",
          "crowd",
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          "experience",
          "family",
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          "first",
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         "name": "",
         "parents": [
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          "",
          "",
          "",
          "",
          "",
          "",
          "",
          "",
          "",
          "",
          ""
         ],
         "type": "treemap",
         "values": [
          958,
          811,
          825,
          906,
          863,
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          761,
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          771,
          862,
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          779,
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        }
       ],
       "layout": {
        "height": 600,
        "legend": {
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "template": {
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             "line": {
              "color": "#E5ECF6",
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            "type": "bar"
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          "barpolar": [
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    "#renders a treemap of most frequently occuring words sans stopwords\n",
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    "tree.write_html(\"Resources/treemap.html\")\n",
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      "100%|███████████████████████████████████████████████████████████████████████████| 4004/4004 [00:00<00:00, 15147.81it/s]\n"
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    "npt.bar_ngram(title='uni-gram', ngram=1, top_n=50, stopwords=stopwords)\n",
    "bgram = npt.bar_ngram(title='Frequently Co-occurring Words', ngram=2, top_n=50, stopwords=stopwords)\n",
    "bgram.write_html(\"Resources/filtered_common_bigrams.html\")\n",
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   "cell_type": "code",
   "execution_count": null,
   "id": "considerable-australia",
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