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moving files from py to notebook

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pull/55/head
Jen Looper 3 years ago
parent ad372f4863
commit 05dcd88fa2
5 changed files with 298 additions and 113 deletions
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57
6-NLP/5-Hotel-Reviews-2/Hotel_Dataset_Filtering.py
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import pandas as pd
import time
import ast
def replace_address(row):
if "Netherlands" in row["Hotel_Address"]:
return "Amsterdam, Netherlands"
elif "Barcelona" in row["Hotel_Address"]:
return "Barcelona, Spain"
elif "United Kingdom" in row["Hotel_Address"]:
return "London, United Kingdom"
elif "Milan" in row["Hotel_Address"]:
return "Milan, Italy"
elif "France" in row["Hotel_Address"]:
return "Paris, France"
elif "Vienna" in row["Hotel_Address"]:
return "Vienna, Austria"
else:
return row.Hotel_Address
# Load the hotel reviews from CSV
start = time.time()
df = pd.read_csv('Hotel_Reviews.csv')
# dropping columns we will not use:
df.drop(["lat", "lng"], axis = 1, inplace=True)
# Replace all the addresses with a shortened, more useful form
df["Hotel_Address"] = df.apply(replace_address, axis = 1)
# Drop `Additional_Number_of_Scoring`
df.drop(["Additional_Number_of_Scoring"], axis = 1, inplace=True)
# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values
df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')
df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
# Process the Tags into new columns
# The file Hotel_Reviews_Tags.py, identifies the most important tags
# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends,
# Family with young children, Family with older children, With a pet
df["Leisure_trip"] = df.Tags.apply(lambda tag: 1 if "Leisure trip" in tag else 0)
df["Couple"] = df.Tags.apply(lambda tag: 1 if "Couple" in tag else 0)
df["Solo_traveler"] = df.Tags.apply(lambda tag: 1 if "Solo traveler" in tag else 0)
df["Business_trip"] = df.Tags.apply(lambda tag: 1 if "Business trip" in tag else 0)
df["Group"] = df.Tags.apply(lambda tag: 1 if "Group" in tag or "Travelers with friends" in tag else 0)
df["Family_with_young_children"] = df.Tags.apply(lambda tag: 1 if "Family with young children" in tag else 0)
df["Family_with_older_children"] = df.Tags.apply(lambda tag: 1 if "Family with older children" in tag else 0)
df["With_a_pet"] = df.Tags.apply(lambda tag: 1 if "With a pet" in tag else 0)
# No longer need any of these columns
df.drop(["Tags", "Review_Date", "Review_Total_Negative_Word_Counts", "Review_Total_Positive_Word_Counts", "days_since_review", "Total_Number_of_Reviews_Reviewer_Has_Given"], axis = 1, inplace=True)
# Saving new data file with calculated columns
print("Saving results to Hotel_Reviews_Filtered.csv")
df.to_csv(r'Hotel_Reviews_Filtered.csv', index = False)
end = time.time()
print("Filtering took " + str(round(end - start, 2)) + " seconds")

56
6-NLP/5-Hotel-Reviews-2/Hotel_Reviews_Sentiment_Analysis.py
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import time
import pandas as pd
from nltk.corpus import stopwords
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# Create the vader sentiment analyser (there are others in NLTK you can try too)
vader_sentiment = SentimentIntensityAnalyzer()
# Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text.
# Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
# There are 3 possibilities of input for a review:
# It could be "No Negative", in which case, return 0
# It could be "No Positive", in which case, return 0
# It could be a review, in which case calculate the sentiment
def calc_sentiment(review):
if review == "No Negative" or review == "No Positive":
return 0
return vader_sentiment.polarity_scores(review)["compound"]
# Load the hotel reviews from CSV
df = pd.read_csv("Hotel_Reviews_Filtered.csv")
# Remove stop words - can be slow for a lot of text!
# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches
# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends
start = time.time()
cache = set(stopwords.words("english"))
def remove_stopwords(review):
text = " ".join([word for word in review.split() if word not in cache])
return text
# Remove the stop words from both columns
df.Negative_Review = df.Negative_Review.apply(remove_stopwords)
df.Positive_Review = df.Positive_Review.apply(remove_stopwords)
end = time.time()
print("Removing stop words took " + str(round(end - start, 2)) + " seconds")
# Add a negative sentiment and positive sentiment column
print("Calculating sentiment columns for both positive and negative reviews")
start = time.time()
df["Negative_Sentiment"] = df.Negative_Review.apply(calc_sentiment)
df["Positive_Sentiment"] = df.Positive_Review.apply(calc_sentiment)
end = time.time()
print("Calculating sentiment took " + str(round(end - start, 2)) + " seconds")
df = df.sort_values(by=["Negative_Sentiment"], ascending=True)
print(df[["Negative_Review", "Negative_Sentiment"]])
df = df.sort_values(by=["Positive_Sentiment"], ascending=True)
print(df[["Positive_Review", "Positive_Sentiment"]])
# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)
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)
print("Saving results to Hotel_Reviews_NLP.csv")
df.to_csv(r"Hotel_Reviews_NLP.csv", index = False)

0
6-NLP/5-Hotel-Reviews-2/notebook.ipynb
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143
6-NLP/5-Hotel-Reviews-2/solution/notebook-filtering.ipynb
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{
"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
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import time\n",
"import ast"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def replace_address(row):\n",
" if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
" return \"Amsterdam, Netherlands\"\n",
" elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
" return \"Barcelona, Spain\"\n",
" elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
" return \"London, United Kingdom\"\n",
" elif \"Milan\" in row[\"Hotel_Address\"]: \n",
" return \"Milan, Italy\"\n",
" elif \"France\" in row[\"Hotel_Address\"]:\n",
" return \"Paris, France\"\n",
" elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
" return \"Vienna, Austria\" \n",
" else:\n",
" return row.Hotel_Address\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the hotel reviews from CSV\n",
"start = time.time()\n",
"df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# dropping columns we will not use:\n",
"df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Replace all the addresses with a shortened, more useful form\n",
"df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Drop `Additional_Number_of_Scoring`\n",
"df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
"# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
"df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
"df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Process the Tags into new columns\n",
"# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
"# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
"# Family with young children, Family with older children, With a pet\n",
"df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
"df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
"df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
"df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
"df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
"df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
"df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
"df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# No longer need any of these columns\n",
"df.drop([\"Tags\", \"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Saving new data file with calculated columns\n",
"print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
"df.to_csv(r'Hotel_Reviews_Filtered.csv', index = False)\n",
"end = time.time()\n",
"print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
]
}
]
}

155
6-NLP/5-Hotel-Reviews-2/solution/notebook-sentiment-analysis.ipynb
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@ -0,0 +1,155 @@
{
"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
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import pandas as pd\n",
"from nltk.corpus import stopwords\n",
"from nltk.sentiment.vader import SentimentIntensityAnalyzer\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create the vader sentiment analyser (there are others in NLTK you can try too)\n",
"vader_sentiment = SentimentIntensityAnalyzer()\n",
"# Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. \n",
"# Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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": null,
"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": null,
"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": null,
"metadata": {},
"outputs": [],
"source": [
"end = time.time()\n",
"print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": null,
"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": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
"df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
]
}
]
}
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