sentiment2

import matplotlib.pyplot as plt #show the plot
import pandas as pd #in order to read the CSV file and use correlation
import seaborn as sns #create a plot


#read Hotel Reviews file
HotelReview = pd.read_csv('C:\Users\pamel\Desktop\College Papers\Data Analytics\\Hotel_Review_Final.csv')


#create x and y axis
x = HotelReview['Review_Sentiment_Score']
y = HotelReview['Reviewer_Score']


sns.distplot(x)
plt.show()

sns.distplot(y)
plt.show()


#create a kde plot
g = sns.jointplot(x, y, data=HotelReview, kind="kde", xlim=(-0.5,1.15), ylim=(6.5,11))
plt.show()


import matplotlib.pyplot as plt #show the plot
import pandas as pd #in order to read the CSV file and use correlation
import seaborn as sns #create a plot


#read Hotel Reviews file
HotelReview = pd.read_csv('C:\Users\pamel\Desktop\College Papers\Data Analytics\\Hotel_Review_Final.csv')


#group by each nation and get mean of review sentiment score an reviewer score
HotelReview2 = HotelReview.groupby('Reviewer_Nationality')[['Review_Sentiment_Score','Reviewer_Score']].mean()


#create x and y axis
x = HotelReview2['Review_Sentiment_Score']
y = HotelReview2['Reviewer_Score']


#create an hexbin plot
g = sns.jointplot(x, y, data=HotelReview2, kind="hex", color="b", xlim=(-0.75,1), ylim=(4,10.5))
plt.show()