You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
260 lines
8.9 KiB
260 lines
8.9 KiB
{
|
|
"metadata": {
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.0"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"kernelspec": {
|
|
"name": "python3",
|
|
"display_name": "Python 3.7.0 64-bit ('3.7')"
|
|
},
|
|
"interpreter": {
|
|
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
|
|
},
|
|
"coopTranslator": {
|
|
"original_hash": "705bf02633759f689abc37b19749a16d",
|
|
"translation_date": "2025-09-04T03:10:35+00:00",
|
|
"source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
|
|
"language_code": "ko"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2,
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stderr",
|
|
"text": [
|
|
"[nltk_data] Downloading package vader_lexicon to\n[nltk_data] /Users/jenlooper/nltk_data...\n"
|
|
]
|
|
},
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"True"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 9
|
|
}
|
|
],
|
|
"source": [
|
|
"import time\n",
|
|
"import pandas as pd\n",
|
|
"import nltk as nltk\n",
|
|
"from nltk.corpus import stopwords\n",
|
|
"from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
|
|
"nltk.download('vader_lexicon')\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vader_sentiment = SentimentIntensityAnalyzer()\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# There are 3 possibilities of input for a review:\n",
|
|
"# It could be \"No Negative\", in which case, return 0\n",
|
|
"# It could be \"No Positive\", in which case, return 0\n",
|
|
"# It could be a review, in which case calculate the sentiment\n",
|
|
"def calc_sentiment(review): \n",
|
|
" if review == \"No Negative\" or review == \"No Positive\":\n",
|
|
" return 0\n",
|
|
" return vader_sentiment.polarity_scores(review)[\"compound\"] \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load the hotel reviews from CSV\n",
|
|
"df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Remove stop words - can be slow for a lot of text!\n",
|
|
"# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
|
|
"# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
|
|
"start = time.time()\n",
|
|
"cache = set(stopwords.words(\"english\"))\n",
|
|
"def remove_stopwords(review):\n",
|
|
" text = \" \".join([word for word in review.split() if word not in cache])\n",
|
|
" return text\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Remove the stop words from both columns\n",
|
|
"df.Negative_Review = df.Negative_Review.apply(remove_stopwords) \n",
|
|
"df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Removing stop words took 5.77 seconds\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"end = time.time()\n",
|
|
"print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Calculating sentiment columns for both positive and negative reviews\n",
|
|
"Calculating sentiment took 201.07 seconds\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Add a negative sentiment and positive sentiment column\n",
|
|
"print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
|
|
"start = time.time()\n",
|
|
"df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
|
|
"df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
|
|
"end = time.time()\n",
|
|
"print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
" Negative_Review Negative_Sentiment\n",
|
|
"186584 So bad experience memories I hotel The first n... -0.9920\n",
|
|
"129503 First charged twice room booked booking second... -0.9896\n",
|
|
"307286 The staff Had bad experience even booking Janu... -0.9889\n",
|
|
"452092 No WLAN room Incredibly rude restaurant staff ... -0.9884\n",
|
|
"201293 We usually traveling Paris 2 3 times year busi... -0.9873\n",
|
|
"... ... ...\n",
|
|
"26899 I would say however one night expensive even d... 0.9933\n",
|
|
"138365 Wifi terribly slow I speed test network upload... 0.9938\n",
|
|
"79215 I find anything hotel first I walked past hote... 0.9938\n",
|
|
"278506 The property great location There bakery next ... 0.9945\n",
|
|
"339189 Guys I like hotel I wish return next year Howe... 0.9948\n",
|
|
"\n",
|
|
"[515738 rows x 2 columns]\n",
|
|
" Positive_Review Positive_Sentiment\n",
|
|
"137893 Bathroom Shower We going stay twice hotel 2 ni... -0.9820\n",
|
|
"5839 I completely disappointed mad since reception ... -0.9780\n",
|
|
"64158 get everything extra internet parking breakfas... -0.9751\n",
|
|
"124178 I didnt like anythig Room small Asked upgrade ... -0.9721\n",
|
|
"489137 Very rude manager abusive staff reception Dirt... -0.9703\n",
|
|
"... ... ...\n",
|
|
"331570 Everything This recently renovated hotel class... 0.9984\n",
|
|
"322920 From moment stepped doors Guesthouse Hotel sta... 0.9985\n",
|
|
"293710 This place surprise expected good actually gre... 0.9985\n",
|
|
"417442 We celebrated wedding night Langham I commend ... 0.9985\n",
|
|
"132492 We arrived super cute boutique hotel area expl... 0.9987\n",
|
|
"\n",
|
|
"[515738 rows x 2 columns]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
|
|
"print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
|
|
"df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
|
|
"print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
|
|
"df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Saving results to Hotel_Reviews_NLP.csv\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
|
|
"df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n---\n\n**면책 조항**: \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있지만, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서를 해당 언어로 작성된 상태에서 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역을 사용함으로써 발생하는 오해나 잘못된 해석에 대해 당사는 책임을 지지 않습니다.\n"
|
|
]
|
|
}
|
|
]
|
|
} |