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Data Analysis
on Hotel Reviews
Presented by Tal Maimon, Ben Gambash, Pamela ezra
THANK YOU!
OUR COLLEGE
College of Managment and Academic Studies , Rishon Le'zion.
Economic and Management student.
Dean's list of 2017.
"Cooking ninja"
Tal Maimon
Business student, financial and stock market speciality.
Dean's list of 2017.
"You'll never know your limit unless you push yourself to them"
Ben Gambash
Economic and Management student.
Teamleader of Economic Forum.
"Life isn't about finding yourself, is about creating yourself"
Pamela Ezra
Our data contains 515,000 costumer reviews and
scoring of 1493 hotels across Europe.
The geographical location of hotels is also provided.
columns: hotel adress, review date, average score, hotel name, reviewer nationality, negative review, review total negative word counts, positive review, review total positive word counts, reviewer score, total number of reviews reviewer has given, total number of reviews, tags, days since review, additional number of scoring, lat, lng.
01.
02.
03.
somthing that will matter
something that will change
something we'll be proud of
somthing new
1. There's a diffenence in the reviews' scores and the amount of reviews during the year.
2. There are nations that bring more reviews then others.
3. There's a difference in the scores between business and liesure purpose.
4. There's a difference between the reviewers score and the sentiment score of their reviews.
difrrences on scores between business ans leisure visitors
Top reviewed hotels
average amount of reviews per date
reviewers nationality
correlation between variables
average score per date
we tried to predicted if a visitor came for a business of leisure purpose , our occurancy hit 81%
we created a KPI that reflects the positive ratio of each review
what it means excatly - positive and negative review?
positive words
we created a function that deletes the duplicated values in order to see more clearly what are the positive words and the negative words....
negative words
we can see that we have THE SAME WORDS in both of them....
we can see that our prediction accurancy is 86%! and when we tried use it it succeed recognize positve and negative reviews!
since there is a difference in the positive and negative reviews - we wanted to predict whether the review is positive or negative
now we can really see what matters
another prediction we used was a decision tree:
it's accurancy is 78%
we decided to use VADER library
let's see it more clearly...
we can see the differences between the reviewers' score and the sentiment score
each review had a sentiment score scale from -1 to 1 based on vader parameters
דso after we did all that..
what we can do with it?
trendy positive and negative words can show us what's really matters for visitors when they come to the hotel, by using the positive words we can organize a personal trip based on what other people liked about the area. by using the negative words the hotel will know how to improve.
"
if we know what are the seasons costumers come to our hotel we can prepare correctly and organize our manpower.
we can know who our clients are, and then reach them using personal advertisment.
We have 4 different types of review analysis, we can combine them in order to create a sentiment parater that will help the hotel analyze their costumers review, in that way they'll know how to optimize themselves and reach more clients. We can also create an interactive tool that can do the analysis for the hotel and provide only the main words and their costumer's score.
"
if we have more business or leisure clients we can optimize our hotel features.
by knowing who are the costumers that reviewed the most, we know which visitors needs to be treated in a certain way so our overall score will rise.
QUESTIONS?