1. Decision Making based on Web 2.0 Data:
The Small and Medium Hotel
Management
MarcirioSilveiraChaves
Business and Information Technology Research Centre (BITREC), UniversidadeAtlântica, Portugal
marcirio.chaves@uatlantica.pt
Rodrigo Gomes and Cristiane Pedron
School of Economics and Management, Technical University of Lisbon, Portugal
rodrigompgomes@gmail.com, cdpedron@iseg.utl.pt

2. Context
A framework for Customer Knowledge Management based on Social Semantic Web.
Chaves, MarcirioSilveira; Trojahn, Cássia and Pedron, CristianeDrebes.A Framework for Customer Knowledge Management
based on Social Semantic Web: A Hotel Sector Approach. In: Customer Relationship Management and the Social and
Semantic Web: Enabling CliensConexus. Colomo-Palacios, R.; Varajão, J. and Soto-Acosta, P. (Eds.). p. 141-157, Hershey, PA:
IGI Global, 2012. ISBN: 978-161-35-0044-6
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3. Outline
• ResearchQuestions
• Methodology
• SampleCharacterisation
• Data Analysis
• Findings
•FutureWork
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4. Research Questions
• What are the most valued concepts of the
ontology (CO) (i.e. features) of SMH in the Lisbon
region? What are the most commonly used
qualifiers to mention these concepts?
• What is the consistency between the rating
assigned by customers who do online reviews and
the content of the review of these same
customers?
• What is the customer’s perception (e.g. positive or
negative) about the quality of SMH in the Lisbon
region?
• Do the SMH management use Web 2.0
technologies to provide feedback to their
customers?
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5. Methodology
• Exploratory study
• 1500 distinct reviews on SMH in the Lisbon region
• First phase
– All hotels in the Lisbon region registered in the National
Registry of Tourism up to March 31, 2011.
– Criteria:
• the hotel must be independent
– i.e. not belong to a chain of hotels;
• it must have fewer than 120 rooms;
• it must have at least 30 reviews available for 2010 on Booking.com
and Tripadvisor online pages.
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6. Methodology
• Second phase
– Reviews analysed using the following parameters:
• Polarity: six categories:
positive, negative, mixed, neutral, irrelevant, or
uncertain;
• CO and Relevance of CO: We chose the segments of the
reviews containing relevant CO;
• Strength of the Polarity (for each CO, polarity was
identified in the following categories: very
negative, negative, neutral, positive, or very positive;
• Qualifier of each CO, i.e. the term attributable to a
positive or negative use of the CO (e.g.
cleanliness, size, and silence).
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7. Methodology
• Third phase
– We analysed the sample data through Excel pivot
table function - intersection of several variables.
• SampleCharacterisation
– 1500 distinct reviews from 50 hotels.
– We chose 4039 excerpts from these reviews, all of
them containing CO.
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8. SampleCharacterisation
• Type of customer
Type of customer % Type of customer % Type of customer %
Young couple 19.56 Couple 15.5 Other 0.67
7
Individual 18.50 Family w/ young 4.99 Unable to identify 0.40
travellers children
Mature couple 17.23 Family w/ older children 4.86 Co-workers 0.33
• Concepts of the Ontology
Group of friends 16.63 Extended family 1.26 Total 100.00
CO % CO %
Room 31.3 Service 3.54
Staff 18.5 Bathroom 2.95
Location 16.9 Parking 1.63
Hotel 11 Restaurant 1.16
Breakfast 10.5 Others 2.47
12-Jun-12 Marcirio Chaves - marcirioc@uatlantica.pt 8

9. Data Analysis
• Strength of the Polarity (SP)
(%) Positive Negative Very positive Very negative Neutral
Room 41.22 46.52 6.33 3.24 2.69
Staff 70.01 11.78 15.13 1.74 1.34
Location 65.64 10.28 22.17 0.29 1.62
Hotel 55.16 23.99 16.59 2.47 1.79
Breakfast 32.71 44.71 9.41 5.88 7.29
Service 27.27 60.84 2.80 4.90 4.20
Bathroom 8.40 81.51 2.52 5.04 2.52
Parking 52.38 44.44 3.17 0 0
Restaurant 29.79 53.19 8.51 6.38 2.13
Bar 29.17 54.17 16.67 0 0.00
Swimming Pool 42.11 42.11 10.53 0 5.26
Balcony 50 0 42.86 0 7.14
Check-in 45.45 45.45 0 9.09 0
Service
Others 22.73 59.09 0 4.54 4.54
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10. Data Analysis
• Ratings and Strength of the Polarity (SP)
– Relationshipbetween the evaluations made by the
guests and the strength of the polarityin the
comments.
Strength of the Polarity
(%) Positiv Negativ Very Very Neutra
e e positive negative l
5 58 17 21 0 4
4 55 31 11 1 2
Rating
3 35 53 4 5 2
2 17 61 1 19 2
1 7 75 3 14 0
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11. Data Analysis
• Concepts of the Ontology (CO)
– CO and the country of origin of the reviewer
Breakfas Bathroo
(%) Room Staff Location Hotel t Services m
Portugal 29 17 15 16 9 3 4
UK 33 18 17 10 12 2 2
Brazil 29 20 18 9 10 5 3
USA 32 23 19 9 7 4 1
Spain 30 12 17 16 12 2 6
Ireland 34 22 15 8 11 2 2
The
Netherlands 29 22 16 17 13 1 0
Canada 34 18 16 9 10 5 4
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12. Data Analysis
•Concepts of the Ontology (CO)and qualifiers
• Breakfast • Hotel in general
– diversity of the menu (44%) – price (30%)
– food quality (20%) – cleanliness (17%)
– silence (12%)
– design (11%)
• Parking
– presence/absence (49%)
– price (22%) • Location
– accessibility (11%) – centrality (35%)
– proximity to public
transportation (22%)
– surroundings (16%)
12-Jun-12 Marcirio Chaves - marcirioc@uatlantica.pt 12

13. Data Analysis
• A vertical analysis of the most frequent CO
Room:
– The most frequent qualifiers:
• cleanliness (18%), size (14%), silence (13%), and bed (10%).
– cleanliness, comfort, scenery, and the Internet -
>positive evaluation
– airconditioning, bed and sound proofing ->negative
evaluation.
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14. Data Analysis
• A vertical analysis of the most frequent CO
Staff – The most frequent qualifiers: positive
– Friendliness
– Helpfulness
– Knowledge and indication of sightsandlandmarks
– Knowledge of foreign languages
Staff – The most frequent qualifiers: negative
– lack of professionalism
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15. Data Analysis
• A vertical analysis of the most frequent CO
– Staff: Qualifiers vs. Strength of the Polarity
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16. Data Analysis
• A vertical analysis of the most frequent CO
Location - The most frequent qualifiers: positive or very
positive
– near the city centre
– proximity to access points for public transport
– proximity to the beach
– safe surroundings
– quiet
– luxurious appearance
Location – The most frequent qualifiers: negative and very
negative
– neighbourhood
– unsafe surroundings of the hotel
12-Jun-12 Marcirio Chaves - marcirioc@uatlantica.pt 16

17. Data Analysis
• A vertical analysis of the most frequent CO
– Location: Qualifiers vs. Strength of the Polarity
12-Jun-12 Marcirio Chaves - marcirioc@uatlantica.pt 17

18. Findings
• The SMH management
shouldmonitorreviewswithhigh and
lowratingsequallycarefully.
• The SMH management
shouldincludeskillsidentified in the
onlinereviews in the
applicantprofilewhenrecruiting new
employees, and undertake the training of
existingstaff.
• When SMH management receive a booking for
British guests, theycouldensure the
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19. Findings
• Although the SMH management cannotchange
the location, theycanhighlightanypleasantaspects
of theirlocation.
• Ifbudget is a constraintfor the SMH
management, Room and Service (Staff)
shouldhavepriority.
• Most SMH management donotseem to use hotel
reviewsites to communicatewiththeirguests.
• In-depthanalysisof online reviewscanbeused as
input to improvedecisionmaking.
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20. FutureWork
Online reviews Features SemanticOrientation
• Explicit
Suggestion
• Implicit
Recommend
• Polarity
ation
Intention • Polarity
Hotel
• Very serious
Facility Staff Complaint • Serious
• Moderate
Adminis
Indoor Outdoor • Affect
trative
Facility Facility • Polarity
Staff
• Intensity
• Judgement
Attitude • Polarity
• Multidomain • Intensity
• Multilingual • Appreciation
• Polarity
• Intensity
12-Jun-12 Marcirio Chaves - marcirioc@uatlantica.pt 20

21. Thank you very much for your
attention!!
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