This study used topic modeling and sentiment analysis to analyze 1,600 hotel reviews from TripAdvisor to identify key themes and issues discussed. Five main topics were identified: service, room, location, night, and staff. The reviews were assigned to these topics and then compared against hotel characteristics like type (budget vs luxury) and location. Significant variations were found, such as budget hotels being more prevalent in the "location" topic and luxury hotels more prevalent in "service" and "room" topics. Reviews also varied significantly based on location, with Liverpool mentioning location more, and Cardiff less mentioning night issues. This analysis provides insights into guest experiences that can help hotels improve.

1. External Characteristics: Hotel, City, Type and Gender
AIM
• To investigate the nature and underlying structure of
the hotel guest experience represented in online
traveller consumer reviews
• To apply novel analysis techniques in this area, such as
Topic Modelling and sentiment analysis to investigate
review themes and issues contained within.
• To identify areas frequently mentioned by reviewers
that hoteliers may need to pay close attention to in
order to improve.
RESULTS
Topic Modelling (TM)
• TM uses computer algorithms to identify latent patterns of word occurrence using
distribution of words in text. The content of the reviews is broken down into
different prominent themes, known as topics. Comparing the association of the
reviews with these topics provides an analysis of their stay and any other issues
• For this analysis, different numbers of topics were investigated. Five topics emerged
that were deemed pertinent in the context of hotel reviews (see also Figure 2).
Topic 1: Service: Leading with the ‘service’ term, including also the terms, bar, food
and restaurant, is a topic on the out-of-hotel room facilities.
Topic 2: Room: Leading with the ‘room’ term, including also the terms, food, floor
and bathroom, is a topic focusing on the actual hotel room (not explicitly).
Topic 3: Location: Leading with the ‘location’ term, including also the terms, city,
centre and parking, is a topic on the out of hotel surrounding environment.
Topic 4: Night: Leading with the ‘night’ term, including also the terms, reception,
sleep and noise, is a topic on the evening etc. experiences.
Topic 5: Staff: Leading with the ‘staff’ term, including also the terms, friendly,
breakfast and helpful, is a topic on the staff and common features of hotel stay.
Cardiff University Opportunities Program (CUROP)
METHOD
• Two different hotel types, luxury (Hilton) and budget (Premier
Inn), were chosen across four similar cities (Cardiff, Glasgow,
Liverpool, Sheffield) in different geographical regions.
• 1600 reviews and associated data were then manually collected
from the leading travel review sight TripAdvisor3, 200 from each
hotel.
• Text data was then cleaned and additional stop words then
removed using an iterative process outlined by Xiang et al2
i.e. words with high ambiguity or words that are generic.
• R software was employed in the subsequent data analysis.
• Having automatically assigned the 1,600 reviews to five topics, we considered these groups of reviews
against other characteristics of the hotel and reviewers.
• Statistical significant variation in spread of hotel
chains across topics. Noting Premier Inn prevalent
in “Location” topic, Hilton prevalent in “Service”
and “Room” topics
CONCLUSIONS
• This project has considered an automated
investigation of the pertinence of online reviews, in
relation to hotel and hotel guest reviewer
characteristics/opinions using Topic Modelling.
• The topic modelling has enabled the identification
of understandable topics amongst the reviews,
namely “Service”, “Room”, “Location”, “Night” and
“Staff”.
• When compared against hotel and hotel guest
reviewer characteristics/opinions there does exist
statistically significant variations.
• Hoteliers should place special consideration on
the location of their hotels when marketing to
travellers or locating new builds, as to so may
generate positive eWOM as topics centred around
location received higher scores in our analysis.
ACKNOWLEDGEMENTS
1 Simms and Gretzel. 2013
2 Xiang et al., 2015
3Banerjee and Chua, 2016
Reviews collected from the users of the leading review site
TripAdvisor.com
Figure 1: Wordcloud of all 1600 reviews collected
External Scores: Overall and Value
• Hotel guests also score their stay at a hotel, here over a 1 to 5 rating scale, showing here Overall score and
Value score.
Figure 2: Top six valued words and word clouds of established topics
• Statistical significant variation in spread of reviews
across the four cities. Noting prevalence of
Liverpool and location of the hotels, Limited
mention of Night at Cardiff hotels
• Statistical significant variation in type of hotel
guest across the different topics. Noting
prevalence of couple in room topic, and absence
of Solo in service topic.
• Not statistically significant variation in the reviews
across the topics based on the gender of the
reviewer
• Statistical significant variation in overall score on
hotel stay of guests, across the different topics.
Note high scores when considering Location and
Staff, wide spread of scores on night topic
• Statistical significant variation in value score on
hotel stay of guests, across the different topics.
Note high score when considering Location.
INTRODUCTION
• Online reviews or eWOM are important sources of information
for both the hospitality industry and their potential customers,
user generated content has become increasingly important in
shaping consumer attitudes, behaviours, and decision-making,
with over 90% of purchases being influenced by such
information.1
• Hotels offer essentially homogenous products and services
meaning hotels use guest satisfaction to distinguish themselves
from their competitors.2
• Different market domain levels of hotels often target different
traveller profiles, these different profiles possess varying
preference levels towards different attributes of the hotel.
• This research investigates the use of large scale text analysis to
examine core hospitality management variables to better
understand guest experiences, as represented in online
consumer reviews
Reviewing the Reviews: Can textual analysis of online consumer reviews provide
insight into the experiences of travellers?
Geoffrey Smith, Professor Malcolm Beynon & Dr Kate Daunt
Cardiff Business School
• Shown in Figure 2, for each of the 5 established topics, is the first six words most
associated with it, and also mini-wordclouds showing the most associated 50 word
with each topic.
• Each review has a level of association to each topic, hence the reviews can be
partitioned based on which topic they are most associated with, found to be: Service -
269, Room - 300, Location - 486, Night - 246 and Staff - 299.