OriginalScientificArticle
DubaiRestaurants:ASentimentAnalysis
ofTouristReviews
VinaitheerthanRenganathan
BanasthaliVidyapith,India
vinairesearch@yahoo.com
AmitabhUpadhya
AmericanCollegeofDubai,uae
upadhyaamitabh@gmail.com
An enormous amount of information is available on innumerable travel websites,
social media and blogs, of which a large part is user-generated content. This web
contentholdsgreatpotentialtoassessvisitorsentimentatadestination;asthisiden-
tifies a need for building automated systems to extract unknown sentiments from
thesesources.Sentimentanalysis,whichincludestextminingandnaturallanguage
processing (nlp) techniques, helps in extracting related sentiments from the data
thus stored, in unstructured formats. The extracted sentiment would facilitate bet-
ter tourist decision making and improve customer service and new product devel-
opment for tourism enterprises. This study presents a sentiment analysis model to
extract the hidden sentiments from tourist reviews about restaurants in Dubai that
will guide visitors to the city in taking suitable dining decisions.Sentiment analysis
is carried out by extracting tourist reviews about restaurants in Dubai using a web
scrapingmethodusingtextminingtechniqueswiththehelpoftheRstatisticalsoft-
ware package. The resultant data is further analysed by sentiment analysis tools to
extract the hidden sentiments, which are categorized under eight heads. The senti-
ment analysis helped uncover hidden sentiments along with the frequency of each
sentiment category. It also helped to find the difference between tourist sentiment
scores with respect to different categories of restaurants.The paper provides a sen-
timentanalysismodel whichcanbe usedin thefuturetoextractthereviewsrelated
to other tourism products besides restaurants, such as accommodation, attractions
andaccessibility.
Keywords: touristreviews,Dubairestaurants,sentimentanalysis,textmining,
Rstatisticalpackage
https://doi.org/10.26493/2335-4194.14.165-174
Introduction
The internet is now a necessary source of personal
andprofessionalinformationandthebrisk-pacedevo-
lution of information and communication technolo-
gies (ict) has given rise to Web 2.0 characterized by
participatory contribution or user-generated content
(ugc), or electronic word of mouth (e-wom). Busi-
nesses hitherto enjoyed a monopoly on the informa-
tion they possessed; users themselves now determine
the information they want to see and to consume
Academica Turistica, Year14,No. 2,December 2021 |16 5

Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
(Breda et.al., 2020). According to the International
Telecommunication Union there are approximately
three and a half billion people, or 47 of the world
population, that use the internet,in turn significantly
impacting varioussectorsof the economy and society
includingtourism(Buhalis&Law,2008).Information
and communication technology (ict) and the inter-
net have changed the way individuals and organiza-
tionsinthetourismsectoroperatetoday(Boyer,2014;
Mariani et al., 2014). There are various internet appli-
cations such as search engines, social media websites,
blogsandreviewsitesthathaveprofoundinfluenceon
tourist decision makingin termsof choice of destina-
tion,accommodationandmodeoftravel(Xiangetal.,
2015).Thereareseveralonlineinterfaceswhichenable
tourists to share their experiences in the form of text,
images and videos. This vast user-generated content
is available online in the form of user reviews, com-
ments, feedbacks, messages, posts, and tweets, pro-
viding opportunities for better decision making for
the stakeholders, especially in the tourism sector, but
which cannot be analysed manually and require au-
tomated tools like text mining (Hearst, 1992; 2003).
Text mining (Renganathan, 2017) involves extracting
andanalysinginformationfromtheunstructureddata
such as text, opinions, reviews, and comments that is
notpossiblewiththetraditionalstatisticaltools.
Naturallanguageprocessing(nlp)(Manningetal.,
1999) helps to enable computer systems read and un-
derstandnaturallanguagessuchasEnglish.Sentiment
analysis (Jiang et al., 2021; Artemenko et al., 2020;
Saad&Aref,2020)enablesunderstandingofdifferent
emotions, attitudes and expressions contained in the
textual informationusing text mining and nlp tech-
niques. Sentiment analysis, or opinion mining, (Pang
&Lee,2008)helpsinextractingthehiddensentiments
or opinion from the unstructured data using tools
such as text mining and natural language processing.
This study provides an overview of sentiment analy-
sis,textminingandnaturallanguageprocessinginthe
tourism sector and builds a sentiment analysis model
using a lexicon-based approach (Balasubramanian et
al.,2021;Boseetal.,2020).Opensourcesoftware–R
statisticalpackage(seehttps://www.r-project.org)was
usedtobuild themodel.
LiteratureReview
The application of ‘text mining’ is a tool which is be-
ing used in many tourism researches (Thomaz et al.,
2016) in the areas of destination branding, destina-
tion characteristics, sentiment analysis, tourist online
behaviour, tourist purchasing decisions and tourism
sectormarketingstrategies.Tourismcanbetermedas
a product which is intangible, experiential and per-
ishable (Xiang et al., 2015). Similarly, tourism can be
defined as a product which exists in the form of in-
formation before a tourist makes a purchase decision
(Doolin et al., 2002). Therefore, the online medium
which acts as a mode of communication providing
a platform for the tourism industry in the fields of
marketing the tourism product and services (Carson,
2006) also helps in the formation of tourist opinions
that have greater influence on their purchasing deci-
sions(Cohenetal.,2014;Litvin etal.,2008).
The online behaviour of tourists can be divided
into pre, onsite and post visits to the desired destina-
tion. Tourists share their experiences, opinions, com-
ments and suggestions online after the visit, which
mightbepositive,neutralornegative(Kimetal.2017).
Onlinemedia,includingonlinereviewwebsites,blogs
andsocialnetworks,enabletouriststosharetravelex-
periences in the form of posts, comments, opinions,
photos and videos (Xiang & Gretzel, 2010; Law et al.,
2017) which in turn become a source of information
for future tourists to plan their travels and purchase
thetourismproducts.
Research related to the influence of social media
on tourist online behaviour shows that around 46
of tourists shared their travel related experiences on
social media and 36 of tourists’ choice of destina-
tion is influenced by social media posts (Thomaz et
al., 2016). Tourist purchasingdecisions areinfluenced
bytheopinionsexpressedbyfellowtouristswhoshare
their experience on tourism products such as desti-
nation,accommodationandtravel(Godnov&Redek,
2016).
Text mining tools which act as a base for opinion
mining enable the study of opinions and sentiments
expressed by the tourist (Pang & Lee, 2008; Ye et al.,
2009).Opinionminingclassifiesthetextintopositive,
neutraland negativeclasseswherein the text classifies
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Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
thetextintotwotothousanddifferentclasses(Pang&
Lee,2008).
Information about the destination aids the tourist
inunderstandingthecharacteristicsofthedestination
(Pangetal.,2011)whichtheyintendtovisitinthenear
future.Avastamountofonlineinformationrelatedto
a destination is generated by tourists who visited the
destination recently. Text mining helps in the study
of user-generated content (Choi et al., 2007; Pang et
al., 2011; Xue, 2013) and helps the tourism sector as a
wholetostudytheimpactofuser-generatedcontentin
the growth of the particular destination. Text mining
models are also used to study destination-specific in-
formationfromthetravelogues(Haoetal.,2010).Text
mining modelscan be used asa decisionsupport sys-
tem which helps travel and tourist agents to analyse
the interesting comments given by the tourist online
(Loh et al., 2003). Similarly, it also helps management
in the hospitality sector to develop strategies to im-
prove their services and increase occupancy rates by
analysing tourist opinion queries from future tourists
whoareabouttovisitthedestination,expressedinon-
lineplatformsincludingthenewsgroupspostings(Lau
etal.,2005;Xiang&Pan, 2011; Qi& Ning, 2017).
Thetextminingprocessisdividedintothefollow-
ing phases: searching and retrieving the set of docu-
ments on a given topic, creating a document corpus,
stop word removal, stemming, creating a term docu-
mentmatrix,clusteringofdocuments,findingassocia-
tionbetweendocumentsandcreationofawordcloud
(Salton & McGill, 1983; Aggarwal & Zhai, 2012; Vija-
yaranietal.,2015).
Phase I of the text mining process involves the
searchingand retrievingof documents which contain
comments, opinions and suggestions using an infor-
mationretrievalprocessbasedon the informationre-
quired by the users (Salton & McGill, 1983).
Phase ii of text mining includes pre-processing
of documents which involves the removal of stop
words present in the documents such as ‘and,’ ‘the’
and ‘an,’ etc. (Vijayarani et al., 2015). Stop words are
removed from the documents using methods such as
the classic method and Zip’s law wherein the former
method removes the predefined stop words and the
lattermethodremovesthewordswithhigh TermFre-
quency – Inverse Document Frequency (tf – idf)
value (Salton & Buckley, 1988). The tf – idf of a
term is an important measure in the text mining pro-
cess whichis definedasfollows:
1. tf – idf = Frequency( i)×N/f(i).
2. TermFrequency=Numberoftimesthetermap-
pears in the document in comparison with total
terms in the document.
3. Inverse document frequency = Total number
of documents/number of documents containing
theterminconsideration.
Phase iii ofthetextminingprocessincludesstem-
ming,whichhelpstoidentifytherootofeachtermand
where each term is replaced by its root term. For ex-
ample,‘happiness’or‘happily’isreplacedwith itsroot
word‘happy.’
Phaseivofthetextminingprocessinvolvesprepa-
ration of a term document matrix wherein the rows
present the terms and columns represent the doc-
ument. For example, if the word ‘Dubai’ appears 17
timesinatraveller’sblogarticleondifferentdatesand
there were 50 dates of blog articles that were consid-
ered for the text mining analysis, and out of the 50
documents, 48 contain the term Dubai then:
tf– idf(Dubai)=15 × 50/48 = 15.625.
Thewebscrapingtechniqueenablestheextraction
ofthecontentfromthewebpagesembeddedinHyper-
TextMarkupLanguage(html)tagsandstoreitintext
format(Prameswariet al., 2017).
Natural language processing (Pang & Lee, 2008)
helps in understanding the interaction between the
computer systems and the human language such as
English. Natural language processing techniques in-
volve studying syntactic (grammar), morphological
(different forms of words), semantic (meaning) and
pragmatic(context)aspectswithinagiventext.There
aredifferentapproaches,suchasstatistical,rulebased,
linguisticormixed,usedinthefieldof nlp.
Natural language processing tools are used in the
tourism sector (Pekar & Ou, 2008; Özen and Ilhan,
2020) to obtain tourist evaluations of services and
products offered by hotels and restaurants from the
reviews availablein theonlinemedium.
Academica Turistica, Year 14,No. 2,December 2021 |16 7

Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
Sentiment analysis helps in understanding senti-
ments from the user-generatedcontent (ugc) (Gräb-
ner et al., 2012; Schmunk et al., 2013; Calheiros et al.,
2017;Chenetal.,2020)intheformofopinions,views
and comments available in various online platforms
suchassocialmedia,groupsandblogs,usingtextmin-
ingandnaturallanguageprocessingtechniques.
Sentiment analysis is generally carried out using
machine learning (ml) (Duong & Nguyen-Thi, 2021;
Yi&Liu,2020),deeplearning(Lietal.,2020),lexicon-
based and hybrid (combination of two methods) ap-
proaches. Each method has its own advantages and
disadvantages(Divakaet.al.,2016).
Themachinelearning-basedapproach(Neheetal.,
2020) uses the train and test datasets to classify the
text into positive and negative sentiments. It includes
classifierssuchassupportvectormachines(svm)and
Naive Bayes classifiers (Yusof et al., 2015; Alaei et al.,
2019).
Deeplearningmethods(Karas&Schuller,2021),
which are similar to machine learning methods, are
alsousedforsentimentanalysis.Deeplearningismore
powerful in terms of classification accuracy (Zhang
et al., 2018). It includes convolutional neural network
(cnn), reinforcement learning, and long short term
memory(lstm)modelsforclassificationpurposes.
Thefollowingaretheadvantagesofmachinelearn-
ing (ml) and deep learning (dl) methods (Yi & Liu,
2020):
1. They are fastertoimplement.
2. They can handlelargevolumesofdata sets.
3. Training accuracy increases with the increase of
datasetsize.
Thefollowingarethedisadvantagesofmlanddl
methods:
1. They require the users to provide labels for the
trainingdatasetinsupervisedlearningmodels.
2. Themodelbuiltononedomainmaynotbesuit-
able for anotherdomain.
The lexicon-based approach (Faheem et al., 2020;
Yu et al., 2019) uses language dictionaries to classify
thetextintopositiveornegativesentiments.Following
arethe advantagesof thismethod:
1. Itattachessentimenttoeachword.
2. Itdoesnotneedanytrainingdataset.
3. Easytoimplement(Alessiaetal.,2015).
Thefollowingarethedisadvantagesofthelexicon-
based approach:
1. Itis languagespecific.
2. If any sarcasm is present, it might not capture
that.
Thispaperusesthelexicon-basedapproachincar-
ryingoutthesentimentanalysisasthetouristreviews
arecollectedintheEnglishlanguage.TheNationalRe-
searchCouncilCanada(nrc)Word-EmotionAssoci-
ationLexiconisused(Mohammad&Turney,2013)in
thispaper.
Tourist reviews are available online at social me-
dia websites like Twitter and Facebook and also on
popular sites like Tripadivisor.com, Expedia.com and
Booking.com.Aninterestingandnoteworthyexample
ofsentimentanalysiswascarriedoutbyValdiviaetal.
(2017), uncovering users’ sentiment about three well-
known monuments in Spain: Alhambra, Mezquita
Córdoba, and Sagrada Familia, with the help of user
ratings available at tripadvisor.com. Also, Philander
andZhong(2016)capturedtouristsentimentsthrough
theirtweetsonLasVegasresorts.
Analysisofvariance(anova)isastatisticalmodel
whichisusedtofindoutwhetherthegroupsorcate-
gories in the study differ with respect to the outcome
variable(Sunetal.,2020).Aposthoccomparisontest
is used to test which groups differ among themselves
(Chen& Scovino, 2020).
Methodology
The study aims to find the hidden sentiments within
the tourist reviews on restaurant service and find
whetheranydifferenceamongrestaurantsexistsbased
on the sentiment scores. The study also addresses the
followingresearchquestion:
rq1 Arethereanysignificantdifferencesamongres-
taurantcategories(Indian,Chinese,Italian,Mid-
dleEastandCaféFoodrestaurants)intermsof
sentimentscore?
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Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
Basedontheaboveresearchquestionthefollowing
nullhypothesisandalternativehypothesisareformed
which will be tested using the analysis of variance
method.
h0 Thereisnosignificantdifferenceamongrestau-
rantcategoriesintermsofsentimentscores.
h1 Thereissignificantdifferenceamongrestaurant
categoriesintermsofsentimentscores.
The study involvesextractingtourist reviews from
touristreviewwebsites.Theextractedreviewsarethen
parsedandconvertedintodocuments.Thedocuments
are further analysed to find the hidden sentiments in
the reviews and sentiment scores are computed from
theanalysis.Thestudyalsofocusesonfindingwhether
anysignificantdifferencebetweenrestaurantsexistsin
terms of sentiment scores. The study includes tools
suchaswebscraping,textminingandsentimentanal-
ysisasthethreemethodsarerelatedandformthebasis
for the other method. Web scraping is required to ex-
tract the text from online websites, text mining tools
are required to parse the texts and sentiment analy-
sistoolsarerequiredtoextractthesentimentfromthe
text.
The reviews of tourists about restaurantsin Dubai
are extracted using a web scraping technique from
www.tripadvisor.com for a period of three months.
The sample included tourist reviews data from web-
pagesrelated to differenttypes of restaurants(Indian,
Chinese, Café, Italian and Middle Eastern food-type
restaurants).
The extracted text data was stored in a text file for
further processing. The content of the text files was
then fed into r environment using the ‘readline’ func-
tion.
The resultant text was converted into vector. The
textdatawaspreprocessedbyremovingthestopwords,
numbers,andpunctuationusingthetm_mapfunction
ofthetmpackage.Here,asinglesentenceinthetext
document is treated as one single document for the
analysis purpose. The model produced a word cloud
output which is a graphical representation of terms
present in the reviews, with the font of the words
showing the frequency of occurrence. The resultant
dataisfurtheranalysedby sentimentanalysistoolsto
extract the hidden sentiments, which are categorized
undereightheadings.
Tocarryoutsentimentanalysis,thefollowingbuilt-
in packages were installed through rstudio environ-
ment: tm – text mining package, nlp – natural lan-
guage processing package, Syuzhet – sentiment anal-
ysis package, and ggplot2 – graphical package (see
https://www.rstudio.com).
Sentimentswhicharecategorizedintopositive,neg-
ative,anger,anticipation, disgust,fear,trust,sadness,
andsurprise headings were extracted using the ‘nrc’
dictionary present in the Syuzhet package. The ‘nrc’
function created the sentiment score matrix which is
usedtofindthedifferenceinsentimentsacrossdiffer-
enttypesofrestaurants.
The analysis of variance model is used to test the
difference between the sentiment scores among the
restaurants and a post hoc comparison test – Tukey’s
hsd test – is applied to see which restaurants differ
among them.
ResultsandDiscussion
The sentiment analysis model provided a word cloud
output which is given in Figure 1. The size of each
word in the word cloud indicates the importance or
frequencyofeachwordappearinginthereviewsgiven
by the customers. Four out of five word clouds ex-
pressedpositivesentimentswhereastheChinesefood
restaurants word cloud includes negative words like
‘overpriced’ and ‘terrible.’ The word clouds which are
obtained above are in line with similar studies con-
ductedoncustomerreviewswithrespecttorestaurant
quality(Kamerer,2014;Gadidov&Priestley,2018).
The sentiment analysis model provided the fol-
lowing sentiment score matrix (Table 1) with respect
to sentiment type and restaurant food type, and the
scoresineachcategoryarerepresentedaspercentages.
The positive sentiments come out top in all the food
categories, ranging from 32 to 34.54, and negative
sentimentrangedfrom1.26to4.86.Caféfood-type
restaurant customers expressed positive sentiments
(34.564)comparedtoothertypesofcustomers.Chi-
nese restaurant customers expressed the highest per-
centage of negative sentiments compared to other
types of customers (2.21). Previous researches also
Academica Turistica, Year 14,No. 2,December 2021 |169

Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
CaféFood Restaurant IndianFood Restaurant MiddleEasternFoodRestaurant
ItalianFoodRestaurant
ChineseFoodRestaurant
Figure1 WordClouds forDifferentRestaurantTypes
obtained similar sentiment scores for the eight senti-
mentcategories(Samueletal.,2020;Rayetal.,2020).
Thesentimentscoresarefurtheranalysedusingthe
analysis of variance method which is given in Table
2. The anova method was used to test the hypoth-
esis h1 that the sentiment scores differ with respect
to restaurant category such as Indian, Chinese, Ital-
ianandMiddleEastern.The anova methodwasalso
usedtocheckwhetherthereisanyinteractioneffectin
termsoftypeofsentimentandcategoryofrestaurants.
FromTable2,the anova modelindicatesthatthe
p-valuesofcategoryofrestaurants,sentimenttypeand
interaction effect are less than 0.05. Hence we con-
clude that there is a difference within the sentiment
types andtype ofrestaurantcategory.Thereis alsoan
interactionbetweentypeofsentimentandcategoryof
the restaurant (Qamar & Alassaf, 2020). Since thep-
value for category of restaurant is less than 0.05, we
willrejectthenullhypothesis(h0)butacceptthealter-
native hypothesis (h1) that the category of restaurant
differswithrespecttosentimentscore.
Since there is significant difference among the re-
staurant type in terms of sentiment scores, a multiple
comparison test, Tukey’s hsd test, is used to check
which type of restaurants differ among them. The
Tukey’s hsd results on sentiment scores with respect
to restaurant types are provided in Table 3. The p-
values marked with (*) are statistically significant at
5 levelof significance (Qamar&Alassaf, 2020).
FromtheabovetablewecaninferthatChinese
food-typerestaurantsdifferwithrespecttoCaféfood,
Indian food, and Middle Eastern food restaurants.
Similarly, Italian food restaurants differ with respect
to Café food, and Indian food and Middle Eastern
fooddifferwith respecttoIndian foodasthep values
are less than0.05 (p< 0.05).
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Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
Table1 SentimentScoreswith RespecttoRestaurantFoodTypeExpressedinPercentage
Restaurant Positive Joy Trust Anticip. Surprise Negative Sadness Anger Fear Disgust
Caféfood . . . . . . . . . .
Chinese . . . . . . . . . .
Indianfood . . . . . . . . . .
Italian . . . . . . . . . .
MiddleEastern . . . . . . . . . .
Overall . . . . . . . . . .
Table2 Analysisof Variance
Variables df SumSq MeanSq F Value Pr(>F)
Categoryofrestaurant    . .e
−9
Sentimenttype    . <e
−16
Categoryofrestaurant×sentimenttype    . .
Residuals   
Table3 The MultipleComparison Test –Tukey’s hsd Results
Type of restaurants Upper value Lower value padj. Difference
Chinese–Café . . . .*
Indianfood–Café –. –. . .
Italian–Café . . . .*
MiddleEastern–Café . –. . .
Indianfood–Chinese –. –. –. e
−7
*
Italian–Chinese –. –. . .
MiddleEastern–Chinese –. –. –. .*
Italian–Indian . . . .*
MiddleEastern–Indian . . . .*
MiddleEastern–Italian –. –. . .
Notes *Statisticallysignificantat5levelof significance.
Conclusion
This study provided an overview of sentiment anal-
ysis, text mining tools and natural language process-
ing techniques in the tourism sector. The paper pro-
videdabaseforanalysingthesentimentsofcustomers’
perceptions about restaurant service. It also high-
lighted the difference between restaurants categories
in termsof sentimentscoresusing ananalysisof vari-
ance model. The developedsentimentanalysismodel
for Dubai restaurants can also be extended to extract
reviews related to tourism products such as accom-
modation, attractions and accessibility, with credi-
ble efficiency proving to be of greater utility to the
tourism sector. The study has some limitations. It has
usedonlylimiteddataandonlyonesentimentanalysis
model,whichisbasedonthelexiconapproach.Hence
it could not compare the accuracy of the proposed
model with other models such as machine learning
and deep learning models. Future research can focus
on building a hybrid model which includes both lexi-
Academica Turistica, Year 14,No. 2,December 2021 |171

Vinaitheerthan Renganathan and Amitabh Upadhya Dubai Restaurants
consandthemachinelearningbasedmodelandsothe
accuracyofthepredictedsentimentscanbemeasured.
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