ML-For-Beginners/translations/mo/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb

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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the hotel reviews from CSV (you can )\n",
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to find the most useful tags to keep\n",
    "# Remove opening and closing brackets\n",
    "df.Tags = df.Tags.str.strip(\"[']\")\n",
    "# remove all quotes too\n",
    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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   "source": [
    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
    "# Now split the strings into a list\n",
    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove leading and trailing spaces\n",
    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
   ]
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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   "source": [
    "# Merge the 6 columns into one with melt\n",
    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    {
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     "name": "stdout",
     "text": [
      "The shape of the tags with no filtering: (2514684, 2)\n",
      "                        index   count\n",
      "0                Leisure trip  338423\n",
      "1                      Couple  205305\n",
      "2               Solo traveler   89779\n",
      "3               Business trip   68176\n",
      "4                       Group   51593\n",
      "5  Family with young children   49318\n",
      "6  Family with older children   21509\n",
      "7      Travelers with friends    1610\n",
      "8                  With a pet    1078\n"
     ]
    }
   ],
   "source": [
    "# Get the value counts\n",
    "tag_vc = df_tags.value.value_counts()\n",
    "# print(tag_vc)\n",
    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
    "print(tag_vc[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。\n"
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