New LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "heard-syntax",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install python-Levenshtein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "painful-animation",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nlplot\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "opposed-sussex",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_ID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviewer_Location</th>\n",
       "      <th>Review_Text</th>\n",
       "      <th>Branch</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
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       "      <th>neg</th>\n",
       "      <th>...</th>\n",
       "      <th>word_yet</th>\n",
       "      <th>word_york</th>\n",
       "      <th>word_you</th>\n",
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       "      <th>word_yrs</th>\n",
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       "      <td>Disneyland_California</td>\n",
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       "      <td>Disneyland_Paris</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
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       "      <td>Disneyland_California</td>\n",
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       "      <td>Disneyland_Paris</td>\n",
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       "      <td>India</td>\n",
       "      <td>The HK Disneyland is definitely small, but it'...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>2012</td>\n",
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      ],
      "text/plain": [
       "      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",
       "1           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
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       "4           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "...         ...       ...         ...      ...       ...       ...   \n",
       "3999        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
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       "4002        0.0       0.0    0.432261      0.0       0.0       0.0   \n",
       "4003        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "\n",
       "      word_yummy  word_zero  word_zone  \n",
       "0            0.0        0.0        0.0  \n",
       "1            0.0        0.0        0.0  \n",
       "2            0.0        0.0        0.0  \n",
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       "...          ...        ...        ...  \n",
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       "\n",
       "[4004 rows x 2150 columns]"
      ]
     },
     "execution_count": 24,
     "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": 26,
   "id": "canadian-teach",
   "metadata": {},
   "outputs": [],
   "source": [
    "NPT = nlplot.NLPlot(reviews_df, target_col='Review_Text')\n",
    "npt = nlplot.NLPlot(reviews_df, target_col='review_clean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "reduced-adoption",
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = npt.get_stopword(top_n=30, min_freq=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "working-lawrence",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "4     -0.036832  0.088255       1        1  48.266349\n",
       "1     -0.049089  0.093652       2        1  46.305725\n",
       "2      0.062588  0.122625       3        1   2.399154\n",
       "3     -0.244480 -0.159539       4        1   1.571442\n",
       "0      0.267813 -0.144993       5        1   1.457331, topic_info=           Term         Freq        Total Category  logprob  loglift\n",
       "2           day  3283.000000  3283.000000  Default  30.0000  30.0000\n",
       "21           go  3438.000000  3438.000000  Default  29.0000  29.0000\n",
       "53   disneyland  2687.000000  2687.000000  Default  28.0000  28.0000\n",
       "44         time  3204.000000  3204.000000  Default  27.0000  27.0000\n",
       "37         ride  4104.000000  4104.000000  Default  26.0000  26.0000\n",
       "..          ...          ...          ...      ...      ...      ...\n",
       "116       queue    20.870776  1239.841302   Topic5  -5.1556   0.1442\n",
       "120        take    20.803664  1333.005512   Topic5  -5.1588   0.0685\n",
       "111       magic    20.754475   471.632753   Topic5  -5.1612   1.1051\n",
       "51        worth    20.751014   659.444110   Topic5  -5.1614   0.7698\n",
       "248         two    20.745741   552.817671   Topic5  -5.1616   0.9459\n",
       "\n",
       "[386 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "52        1  0.103956    academy\n",
       "52        2  0.935608    academy\n",
       "3127      1  0.075459   accurate\n",
       "3127      2  0.980967   accurate\n",
       "1562      1  0.300745  admission\n",
       "...     ...       ...        ...\n",
       "305       2  0.240064      young\n",
       "305       3  0.579977      young\n",
       "305       4  0.002124      young\n",
       "4209      1  0.076392        yum\n",
       "4209      2  0.916710        yum\n",
       "\n",
       "[853 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 2, 3, 4, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 19,
   "id": "controlling-sheep",
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 4004/4004 [00:00<00:00, 19813.70it/s]\n"
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    "tree = npt.treemap(title='Tree of Most Common Words', ngram=1, top_n=30, stopwords=stopwords)\n",
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      "100%|███████████████████████████████████████████████████████████████████████████| 4004/4004 [00:00<00:00, 19301.22it/s]\n",
      "100%|███████████████████████████████████████████████████████████████████████████| 4004/4004 [00:00<00:00, 15147.81it/s]\n"
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       "        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": [
    "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",
    "bgram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minimal-young",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:PythonAdv] *",
   "language": "python",
   "name": "conda-env-PythonAdv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}