The information provided by hotels booking websites, such as TripAdvisor.com, in the form of ratings and reviews is not accurate enough to help users to easily make quick decisions. This problem is mainly due to the fact that hotels are often accompanied by a long history of reviews and ratings. In addition, the information contained therein is outdated and obfuscates the recent ratings and sentiments. In this project, I propose to provide users with a search option, that enables to control the timeframe of reviews. Based on this timeframe, users get a list of hotels ranked by an appropriate statistical measure of the reviews' ratings (the often used average value is not a proper measure for an ordinal variable such as the rating ) and for each hotel, the up-to-date sentiments of the hotel features (such as the hotel cleanliness, service, etc.)

1. HotelsNow
Jinane Harmouche
@ Insight 2018, Toronto
Stay in the Present
1

2. 2
Hotel Reviews - Have they made decision easy ?
Studies showed that 95% of travellers regularly use travel review
websites to make their booking decisions
TripAdvisor is the most visited travel website in the world
Number of reviews : 661 millions
Users write an average of 280 new reviews every minute

3. Problem #1
Deciding where to stay among many hotels with similar ratings and
thousands of reviews is difficult.
Number of reviews of top 30 hotels
Boston
# of reviews
Toronto Paris
# of reviews # of reviews
Source: Tripadvisor data scraping 3
#ofhotels
#ofhotels
#ofhotels
70% of hotel reviews give 4 and 5 ratings

4. Obsolete reviews can portray misleading information to the users. In
addition, there is no way for users to know whether hotels quality has
changed over time.
Problem #2
Hotels go through changes, renovations, updates ..
Source: Tripadvisor data scraping 4

5. Impact of new reviews is not reflected for hotels having a large
number of reviews (obfuscation effect).
88 negative reviews required to change
the “Poor” score by 1%
Excellent : 46%
Poor : 6%
8,822
reviews
Problem #3
Source: Tripadvisor data scraping 5

6. Solution
Granular Ranking of Hotels based on a selected time frame
Up-to-Date Sentiment of features for each hotel for a selected time
frame
Trend of Hotels Ratings and Sentiments

7. 7
Granular Ranking of Hotels

8. Current hotels ranking by booking websites
Hotels rating
2018/11/122018/10/01Boston
Source: Tripadvisor data scraping
30 Hotels
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Out of 30 hotels, 27 hotels have ratings 4 and 4.5. It is not easy for users to
decide among 27 hotels.
Check-in date Check-out date
Average rating
• Is not appropriate for ordinal data
• Does not capture difference in
distributions
• Studies showed that the effects of
average review scores decrease
quickly over time
#ofhotels

9. Best hotels
Worst
hotels
Percentile rank of rating 4
Ratings of
Reviews
Percentile
Rank of 4
For
each
hotel
9
Out of 30 hotels, 4 hotels with Perc < 20%
Granular ranking based on selected time frame
Last 6 Months
History
Of Reviews
# of ratings
#ofhotels
Source: Tripadvisor data scraping
#ofhotels

10. 1 2 3 4 5Rating
Counts, hotel1
Counts, hotel2
˜χ2
=
n
∑
k=1
(Ck − Fk)2
Ck + Fk
C1 C2
C3 C4 C5
F1 F2 F3 F4 F5
10
Refine ranking using Chi-Square Statistic
Re-order hotels by increasing value of ˜χ2
Percentile rank of rating 4
Hotels with close percentile rank of 4 may have
different distributions/size of ratings
We should measure distribution similarity
using Chi-Square Statistic
#ofhotels

11. 11
Sentiment of Hotel Features

12. Texts
Last K months
Positive-Negative
classification
Features
Sentiments
12
History of
Reviews
“The carpet in the room
was definitely not clean”
Polarity= -0.18
“The primary reasons we
booked was the location
and history of the hotel”
Polarity= 0.4
Polarity > Threshold ?
Positive
Negative
(Keywords,
scores)
(Keywords,
scores)
Up-to-date sentiments of hotel features
TextBlob, Gensim Python modules

13. 13
Up-to-date sentiments of hotel features
Seaport Boston Hotel
12% of ratings < 4
Omni Parker House
42% of ratings < 4
1st / 30 hotels
27th / 30 hotels
13
Service
Service
Staff
Staff
Cleanliness
Cleanliness
Location
Location
Data Not
Available
Source: Tripadvisor data scraping

14. 14
Trend of Hotel Ratings and Sentiments

15. Reviews rating distributions
Ratings (1-5)
Percentile rank of 4 { 36, 38, 41 }%
Distributions are statistically different
according to
Chi-square statistic
15
Trend of Ratings
How hotels quality change in time ?
Source: Tripadvisor data scraping
#ofhotels
Year 2016 2017 2018
Sample size 1,100 1.360 982

16. 16
2016 2017 2018
43%35%
100%
57%65%
2016 2017 2018
29%22%
71%
100%
78%
2016 2017 2018
100%
2016 2017 2018
35%30%
65%
100%
70%
Service Staff
Cleanliness Location
Positive Sentiment Negative Sentiment
Data Not
Available
Trend of Sentiments
Trend of hotels features will help users understand quality over time
Source: Tripadvisor data scraping

17. Jinane
Postdoc,
Signal decomposition
Ph.D. Signal Processing,
—> Electromechanical systems
M.Sc. Control Theory and Practices,
—> Electrical machines
B.E. Electrical Engineering
Postdoc, Sensor data mining
—> Smart infrastructure
17