The purpose of this exploratory research is to develop a methodology focused on analysing online reviews related to service robots in hotels using Machine Learning techniques to train the data collected from TripAdvisor. Following this methodology, it is possible to predict the new rate given to a hotel considering the robot typology.

1. Machine Learning Techniques and Robot Typologies: Is It Possible To Predict
The New Hotel Rating By Analysing UGC Content From Tripadvisor?
Orea-Giner, Aliciaa b c ; Calero-Sanz, Jorgec e f ; Villacé-
Molinero, Teresaa c d ; Muñoz-Mazón, Anaa c d ; Fuentes-
Moraleda, Lauraa c d
a B u s i n e s s e c o n o mi c s , R e y J u a n C a r l o s U n i v e r s i t y , Ma d r i d , S p a i n ;
A l i c i a . o r e a @u r j c . e s ; t e r e s a . v i l l a c e @u r j c . e s ; a n a . mu ñ o z @u r j c . e s ;
l a u r a . f u e n t e s @u r j c . e s
b E I R E S T , U n i v e r s i t é P a r i s 1 P a n t h é o n - S o r b o n n e , P a r i s , F r a n c e .
c Hi g h P e r f o r ma n c e R e s e a r c h G r o u p O P E NI NNO V A , R e y J u a n C a r l o s
U n i v e r s i t y , Ma d r i d , S p a i n .
d C e n t r o U n i v e r s i t a r i o d e E s t u d i o s T u r í s t i c o s , R e y J u a n C a r l o s
U n i v e r s i t y , Ma d r i d , S p a i n .
e S i g n a l a n d C o mmu n i c a t i o n s T h e o r y a n d T e l e ma t i c S y s t e ms a n d
C o mp u t i n g , R e y J u a n C a r l o s U n i v e r s i t y , Ma d r i d , S p a i n ;
J o r g e . c a l e r o @u r j c . e s
f D e p a r t me n t o f A p p l i e d Ma t h e ma t i c s a n d S t a t i s t i c s , E I A E ,
T e c h n i c a l U n i v e r s i t y o f Ma d r i d , Ma d r i d , S p a i n .

2. 2
Previous research:
Fuentes-Moraleda, L., Diaz-Perez, P., Orea-Giner, A., Munoz-Mazon, A., & Villace-Molinero, T. (2020).
Interaction between hotel service robots and humans: A hotel-specific Service Robot Acceptance Model
(sRAM). Tourism Management Perspectives, 36, 100751. https://doi.org/10.1016/j.tmp.2020.100751
Abstract
The growing implementation of robotics in hospitality and tourism requires broader research into customers' experience with service robots. This
study explores human-robot interaction (HRI) in the context of tourists interacting with hotel service robots. The data, 7994 online TripAdvisor
reviews of 74 hotels, were subjected to a content analysis based on the Service Robot Acceptance Model (sRAM) and its dimensions
(functional, social-emotional and relational). A sentiment analysis was also carried out. The results identify the principal dimensions and
variables involved in HRI and the feelings robots inspire in different types of travellers. Guests most often comment on the functional dimension.
Robots' functions determine this experience and influence the interaction between robots and hotel guests.
Keywords
Service robotsHRI (human-robot interaction)HotelssRAM (Service Robot Acceptance Model)Content analysis

3. 1 . I N T R O D U C T I O N
• Industry 4.0 tools allow automated production processes (Xu et al., 2018) and Artificial
Intelligence (AI) approaches are a crucial aspect of analysing the tourism industry
(Nilashi et al.m 2017).
• The customer experience is a crucial aspect to be analysed to contribute to the Human-
Robot interaction (HRI) literature (Tussyadiah and Park, 2018; Ivanov et al., 2019).
• Previous studies have been focused on online reviews from TripAdvisor (Yu, 2020;
Fuentes-Moraleta et al., 2020; Park, 2020; Valtan and Dogan, 2021), showing that it is
possible to detect the customer's perceptions about service robots from UGC analysis.
Room Service Robot
4

4. • According to the literature, using robots presents different positive aspects
of their availability (Belante et al., 2019). The increase in the number of
robots used in hotels and other tourism services shows that service robots
are becoming increasingly important (Park, 2020).
• The machine learning approach is used in the tourism context to analyse
and predict the behaviour of costumers (Zhang et al., 2019; Arefieva et al.,
2021)
5
2 . L I T E R A T U R E R E V I E W

5. RQ. Is it possible to predict the new rating given to a hotel considering the
service robot typology using Machine Learning techniques?
7
3 . R E S E A R C H Q U E S T I O N
The purpose of this exploratory research is to develop a methodology
focused on analysing online reviews related to service robots in hotels using
Machine Learning techniques to train the data collected from TripAdvisor.
Following this methodology, it is possible to predict the new rate given to a
hotel considering the robot typology.

6. M a c h i n e L e a r n i n g c l a s s i f i e r
N a ï v e B a y e s
8
4 . M E T H O D O L O G Y

7. 9
5 . R E S U L T S
Naïve Bayes modelling Accuracy: 60.02%
‘Room service’ and ‘cloak room’ are associated with a higher rating given to the hotel

8. 10
6 . C O N C L U S I O N S
RQ. Is it possible to predict the new rating given to a hotel
considering the service robot typology using
Machine Learning techniques?
Yes, but there are some limitations that must be
considered, and we are currently working on
improving our database and model.

9. 11
7 . L I M I T A T I O N S A N D F U T U R E L I N E S
Future research might apply more Machine Learning classifiers:
• K-nearest neighbors (KNN)
• Decision tree
• Logistic regression
• Neural network (NN)
• Support vector machine (SVM)
• Random forest
• Gradient boosting.
The database and the attributes might be improved to obtain a higher accuracy level.

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Acceptance Model (sRAM). Tourism Management Perspectives, 36, 100751.
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(2020). Interaction between hotel service robots and humans: A hotel-specific Service Robot
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12. pattern
Thank you
Contact: Alicia.orea@urjc.es