Unraveling Tourists' Preferred Homestay Attributes from Online Reviews: A Sentiment Analysis Approach

Abstract

The purpose of this paper is to identify mostly talked about general and specific attributes of homestay accommodations in online tourist reviews and to bring an understanding on sentiments attached to them. It offers insightful thought on tourists/customer preferred attributes which in turn helps in designing and delivering better homestay services. Sample of 14,084 reviews on homestays located in nine different states of India are extracted from Trip Advisor.in website. Quantitative content analysis based on frequency of words is conducted to get an understanding on mostly spoken about homestay attributes. Further, set of 'tidy' and 'text/sentiment mining' tools are used to approach and infer the emotional content of reviews as whether the part of text is positive or negative, or even more subtle level difference in emotion like joy or fear. Correlation test was conducted to gain quantitative outlook of similarities and differences between the sets of frequent words in reviews of homestays. Application of sentiment analysis resulted in categorizing the sets of words into eight different emotional categories depicting four positive and four negative emotions. The words in these categories were found to have certain associations with various attributes of homestay accommodation. Broadly, fourteen attributes were found to be mostly talked about in sets of words extracted from online reviews signifying that they had strong connection in creation of positive and negative sentiments towards homestays. Words with high frequencies were more specifically related to the attributes of homestay accommodation. As the frequency of words decreased, they were found to be more related to attributes of the homestay location or the destination. Correlation coefficient of word frequencies suggested that majority of reviews were done on common attributes of homestays irrespective of geographical location. Practical and research implications are provided.

Full text

Unraveling Tourists
‟
Preferred Homestay Attributes
from Online Reviews: a Sentiment Analysis
Approach
1Bimal Thapa, 2Anicar D Manavi and 3D.H. Malini
1,2,3School of Management,
Pondicherry University, India.
Abstract
The purpose of this paper is to identify mostly talked about general and
specific attributes of homestay accommodations in online tourist reviews
and to bring an understanding on sentiments attached to them. It offers
insightful thought on tourists/customer preferred attributes which in turn
helps in designing and delivering better homestay services. Sample of
14,084 reviews on homestays located in nine different states of India are
extracted from Trip Advisor.in website. Quantitative content analysis based
on frequency of words is conducted to get an understanding on mostly
spoken about homestay attributes. Further, set of ‘tidy’ and ‘text/sentiment
mining’ tools are used to approach and infer the emotional content of
reviews as whether the part of text is positive or negative, or even more
subtle level difference in emotion like joy or fear. Correlation test was
conducted to gain quantitative outlook of similarities and differences
between the sets of frequent words in reviews of homestays. Application of
sentiment analysis resulted in categorizing the sets of words into eight
different emotional categories depicting four positive and four negative
emotions. The words in these categories were found to have certain
associations with various attributes of homestay accommodation. Broadly,
fourteen attributes were found to be mostly talked about in sets of words
extracted from online reviews signifying that they had strong connection in
creation of positive and negative sentiments towards homestays. Words
with high frequencies were more specifically related to the attributes of
homestay accommodation. As the frequency of words decreased, they were
found to be more related to attributes of the homestay location or the
destination. Correlation coefficient of word frequencies suggested that
majority of reviews were done on common attributes of homestays
irrespective of geographical location. Practical and research implications
are provided.
Key Words:Homestay, sentiment analysis, online reviews, e-WOM.
International Journal of Pure and Applied Mathematics
Volume 119 No. 15 2018, 1567-1585
ISSN: 1314-3395 (on-line version)
url: http://www.acadpubl.eu/hub/
Special Issue http://www.acadpubl.eu/hub/
1567

1. Introduction
Emergence of Web 2.0 has resulted in enormous amount of user generated
online content(Shan, Ren, & Li, 2016). In tourism sector, it has enabled tourists
to share their reviews regarding various tourism products online(Rahmani,
Gnoth, & Mather, 2017).Homestay is one such key product of cultural tourism
and is a fastest growing accommodation segment in Tourism industry (Wang,
2007; Rizal, Yussof, Amin, & Chen-Jung, 2018).With remarkable growth of
sharing economy in tourism industry and homestay being a part of it, has seen
tremendous increase in demand, since travellers of today desire to engage in
meaningful interactions with locals, and to get authentic experience unique to a
destination (Hasan, Sabtu, & Sahari, 2016; Tussyadiah & Pesonen, 2016).
Homestay has taken a form of product diversification in accommodation sector
and has contributed in growth of tourism as well as regional development
(Oguchaa, Riungub, Kiamaa, & Mukolwe, 2015). Homestay has been the
effective tool in alleviating poverty through income generation especially
among rural poor population and hence is considered as pro-poor tourism
concept(Bhalla, Coghlan, & Bhattacharya, 2016; Truong, Hall, & Garry, 2014).
Homestay is also considered as pro-women tourism concept because of its
contribution towards women empowerment by fostering gender equality
(Acharya & Halpenny, 2013).In top fifty tourist destinations in India, homestays
have taken up as much as 13% share in accommodation sector (Pratap, 2016).
The growing importance of the homestay can be realised by the benefits it
brings to the host communityand hence it is important that sustainability of this
accommodation sector is ensured for consistent development of the community
in which the concept is implemented(Agyeiwaah, Akyeampong, Boakye, &
Adu-Gwamfi, 2014). Sustainability can be ensured by constant
improvement(Abreu, Martins, Fernandes, & Zacarias, 2013). In hospitality
industry, sustainability can be achieved through continuous improvement in
service operations (Chen, Sloan, & Legrand, 2010) resulting in improved brand
image which ensures continuous inflow of tourists.For homestay concept to be
sustainable, great attention and strong strategies are needed to develop it
further(Samsudin & Maliki, 2015)as homestay accommodationis aspecial
interest tourism product and is experiential in nature as opposed to traditional
hotel accommodations(Jamal S. a., 2011; Guttentag, 2015). To improve the
services, it is necessary to understand the needs and expectations of tourists
regarding facilities provided by the homestaysand prioritize continuous
improvement in service attributes that are of utmost importance to
them(Molina-Azorín, Tarí, Pereira-Moline, Gamero, & Pertusa-Ortega, 2015).
With the growth of internet based accommodation reservation system in last
decade, user generated online reviews popularly known as Online Consumer
Reviews (OCR) or electronic Word of Mouth (e-WOM) has become one
powerful tool to understand needs, feelings and expectations of tourists
(Gössling & Lane, 2014).Online reviews have strong influence on decision
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making by travelers‟ while selecting an accommodation during their travel
(Gretzel & Yoo, 2008). Through online reviews, travelers who actually stayed
in accommodations talk about attributes and facilities which becomes the
information base for other travelers during the process of comparison and
selection (Filieri & McLeay, 2014).Online reviews reduces the information gap
between marketers and customers and facilitate useful exchange of information
between them which helps marketers to design their products and services that
best suits the needs of customers (Labrecque, Esche, Mathwick, Novak, &
Hofacker, 2013). Study of online reviews brings an understanding on what
made tourists to post them online, which in turn help hosts to improve and
strengthen their services (Park & Allen, 2012).
Homestays have also become the part of internet based reservation system
(Kline, Morrison, & John, 2005) and a substantial amount of user reviews has
been generated and available online. Comprehending the growing importance of
homestay services and mounting academic interest in the study of online
reviews as e-word of mouth, authors of this paper determined to understand
homestay attributes those are often talked about in online reviews and
sentiments of the reviewers towards the accommodation. Further, literature gap
in this framework was revealed in the literature review section. To meet the
objective, quantitative content analysis approach is followed. The results of the
same are presented. Lastly, findings are discussed on the relative impact of
online reviews from both academic and managerial perspectives.
2. Literature Review
Homestay is an accommodation arrangement in which tourists stay like a family
member in homes of local residents in a destination, eat food, experience daily
ways of host‟s life (Gu & Wong, 2006)in exchange for a payment (Andriotis &
Agiomirgianakis, 2013). According to (Jamal, 2011), homestay tourism is a
form of tourism which attracts a particular segment of tourism market in which
people desire for authentic experiences as it is based on nature, culture and local
custom. Unlike other accommodation options, homestay provides opportunity
for tourists to learn about local life and culture (Kontogeorgopoulos, Churyen,
& Duangsaeng, 2015).Generally, idle rooms in the host‟s private homes are
provided to interested tourists (Hjulmand, Nielsen, Vesterlokke, Busk, &
Erichsen, 2003). This becomes an opportunity for hosts to earn some additional
income and also to meet people from across culture(Lanier & Berman, 1993;
Gan, Inversii, & Rega, 2018). Homestay has gained substantial attention from
researchers following its growing demand (Mura, 2015). A number of empirical
studies have been conducted to understand which attributes of homestays attract
tourists to choose the accommodation. Homely atmosphere, personalized
services, home cooked food, authentic local experiences, cultural immersion
have remained the top reasons among tourists to choose a homestay
accommodation while travelling(Wang, 2007; Gunasekaran & Anandkumar,
2012; Agyeiwaah, 2013). Although homestays is a part of experiential &
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cultural tourism(Wang, 2007), empiricial findings suggest that price also has a
role to play while tourists decide to stay in a homestay accommodation(Hsu &
Lin, 2011; Rasoolimanesh, Dahalan, & Jaafar, 2016). Some other reasons
concluded by previous researches are quiet local neighbourhoods (Tussyadiah
& Pesonen, 2016), less pollution(Agyeiwaah, 2013), scenery, attractions, liesure
and relaxation (Hsu & Lin, 2011). Although studies have been done on why
tourist choose homestays but their sentiments towards the accommodation has
not been studied yet. One way to know sentiments towards certain product or
service is sentiment analysis using online consumer reviews (Yu, Duan, & Cao,
2013).
Online consumer reviews (OCR) are the big data source for stakeholders of
business (Qi, Zhang, Jeon, & Zhou, 2016). They have implication for
businesses especially on sales and business performance (Ye, Law, Gu, & Chen,
2011). In case of customers, it helps in their purchase decision. Analyzing OCR
is a challenging task due to its volume, variety, velocity and veracity (Qi,
Zhang, Jeon, & Zhou, 2016). This led to the core establishment of Big-data
commerce, which can help in extracting real time insights from big data to drive
more profitable business decisions. In case of tourism-accommodation sector,
OCR has impact on rate of hotel booking and hotel performance (O'Connor,
2008). It has become handy to the travelers in their travel planning specifically
while choosing accommodation (Gretzel & Yoo, Use and Impact of Online
Travel Reviews, 2008). The negative/positive sentiments expressed in the
reviews can influence booking intention and trust among readers about
particular service provider (Lee, Law, &Murphy, 2011). Performance of any
business can be improved not only by understanding the initial expectation of
customers but also by learning from the customers‟ word-of-mouth about their
products. OCRs are more user-oriented and describe the product in terms of
different usage scenarios and assess it from a user‟s perspective (Chen & Xie,
2008). Thus they are popularly called electronic word-of-mouth and gives
insights on consumer preferred attributes of a service and their sentiments
towards them (Liu & Karahanna, 2017). In manufacturing sector, the
description or the opinion mentioned on the product attributes are being
considered for product improvement or product development(Qi, Zhang, Jeon,
& Zhou, 2016). In homestay accommodation sector too, OCRs have
implications to operators on attributes those need improvements or facilities
those are to be added by analyzing the reviews.
3. Methodology
Quantitative content analysis was carried out to identify the attributes of
homestay services.14,084 reviews on homestays in India were obtained from
TripAdvisor.in through web scrapping. The purpose of the paper is to study
sentiments towards various attributes of homestays in entire nation. Hence,
effort was made to collect reviews across locations in India to ensure
maximum coverage. Based on availability of reviews on the website; the
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states of Assam, Himachal Pradesh, Jammu & Kashmir, Karnataka, Kerala,
Rajasthan, Sikkim, Uttar Pradesh, and Uttarakhand were selected. The data
was extracted and analyzed using R software. R commands were taken from
the works of Silge & Robinson (2018), Hadley & Renkun-ken (2015),
Peterson (2017), and Claudia (2017) and customized as per the need of this
work.
Tidy text format approach is employed to analyze the review text. The tidy
text format is defined as “a table with one-token-per-row. A token is a
meaningful unit of text, such as a word, that we are interested in using for
analysis, and tokenization is the process of splitting text into tokens”. Further,
set of „tidy‟ and „text mining‟ tools are used to approach and infer the
emotional content of reviews as whether the part of text is positive or
negative, or even more subtle level difference in emotion like joy or fear.
Lexicons consist of many English words to which scores are assigned for
positive or negative sentiment, and also possibly emotions like joy, anger,
sadness, and so forth. The “nrc” (Mohammad & Turney, 2013) and “bing”
(Liu) lexicons categorize words in a binary fashion (“yes”/“no”). Based on
this, “nrc” lexicon classifies words into two sentiments (positive and
negative) and eight emotions (anger, anticipation, disgust, fear, joy, sadness,
surprise, and trust) categories. The bing lexicon classifies words into positive
and negative categories. Both lexicons have more negative than positive
words, but the ratio of negative to positive words is higher in the „bing‟
lexicon than the „nrc‟ lexicon. This will contribute to the higher systematic
difference in word matches (Silge & Robinson, 2018). Hence net sentiment of
homestay reviews is analyzed using bing lexicon, and to present more
nuanced emotions, nrc lexicon is used.
Single review is a very small section of text, thus it may not have enough
words in it to get a good estimate of sentiment. For this reason, reviews of
each state were combined together and every 50 lines were taken as one
section. Next, counts of positive and negative words from each section were
obtained. An „index‟ was defined to keep track of on which review we are
present; this index (using integer division) counts up sections with 50 lines of
text. Then we calculated net sentiment i.e. positive minus negative.
Frequency of words in the reviews gives a broader knowledge on most
spoken attributes and the sentiment attached to them with regard to homestay.
Word frequency ranged from 13478 to 1. As frequency of words reduced,
the words were found to be more particular to the unique attribute of
destination. Correlation test was conducted to gain quantitative outlook of
similarities and differences between the sets of word frequencies in the
reviews of homestays located in nine states. For this purpose, „proportion‟
was calculated by dividing frequency of a word (n) with total number of
words (sum(n)).
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4. Content Analysis
Sentiment Analysis
Sentiment scores are plotted across the plot trajectory of each state (Figure
1).Net sentiment result of each state is plotted against the index on the x-axis
that keeps the track of review time in text sections. Homestays in all nine
states have got more positive reviews than negative. The chart exhibits peaks
and dips in the sentiment. This might be due to the excellent/poor service
quality from host side or changes in tastes, preferences or perception among
the visitors. To get better picture on reasons for the variation in sentiment
score, reviews were further programmatically analyzed by applying „nrc‟
lexicon and results are presented in the form of word cloud. Of the eight
emotions given in „nrc‟ lexicon, words of four emotions namely joy,
anticipation, surprise and trust were categorized as positive sentiment (Figure
2), and the rest four emotions, anger, disgust, fear and sadness are categorized
as negative sentiment (Figure 3)(Mohammad & Turney, 2013). Certain
attributes of homestay in stills certain emotions, this is inferred from the size
of a word‟s text in proportion to its frequency within its sentiment.
Figure 1 : State Wise Homestay Sentiment Plot
From positive sentiment word cloud (Figure 2), attributes those signified
„joy‟ are delicious, food, clean, beautiful, love, helpful, welcomed, luxurious,
friendly, massage, smiling, green, garden and authentic. This indicates that
offering a warm welcome to the guest, providing excellent services with
smile and love, being helpful to the guests, providing authentic local
experiences, giving authentic information as per guests‟ needs, beautiful
location, luxurious ambience, family maintained gardens, greenery around the
location and most importantly delicious food and cleanliness creates
joyfulness among visitors. Tourists‟ expectations from the homestay can be
understood by going through the most frequent words used in „anticipation‟
lexicon. Tourists anticipate well planned vacation with perfection. The words
prepared, time, immediate, efficient signifies anticipation of hosts being
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proactive and efficient in providing services and handling uncertain
circumstances. Visitors have some expectations regarding neighborhood and
people in the destination since staying in a homestay provides opportunity to
interact with locals. Delightful experiences make the tourists more satisfied
and loyal. The „surprise‟ words in the picture highlight that tourists visit the
homestays for getting/giving memorable moments for their loved ones.
Uniqueness of the homestay location such as local festivals, scenic beauty,
natural vegetation, shopping, and invitation have the potential to surprise the
guests. Reasonable pricing and providing more than expected definitely
surprise guests. „Trust‟ is one of the most important antecedents of tourists‟
visit, revisit or recommendation. Aspects those contribute in creating trust are
word-of-mouth i.e. recommendation from others, taking care of visitors by
hosts in terms of hospitality, safety and security, providing personal touch by
being always attentive to them, giving proper suggestions, holiday budget,
and behavior of staff & host family members. These altogether contribute for
building up positive sentiment.
Figure 2 : Positive Sentiment Wordcloud
The study of homestay service attributes which generates negative emotions is
as important as attributes those generate positive emotions. These emotions in
the form of anger, disappointment or regret and worry are generated if the
customer is dissatisfied with the service (Mattila & Ro, 2008). The same is
expressed online in the form of negative reviews on the service provider. Thus
an effort is made to extract the attributes those contributed for negative
emotions among the visitors of homestay. The negative sentiment word cloud
(Figure 3), presents four segment of words representing four different negative
emotions; anger, disgust, fear and sadness. The words in the „anger‟ segment of
word-cloud are hot, money, words, noisy, politics, broken, strike, complaint,
chaotic, annoying, fee, disturbed and confusion. These words shows that
extreme weather conditions, unreasonable price structure that does not result in
value for money, noisy locality, high fees on activities and nearby attractions,
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difficulty in reaching the destination, failure in handling concerns of guests by
some hosts, local politics and strikes creating chaotic situation during travel,
and broken& ill maintained infrastructure can make the visitors angry. Words
in the disgust lexicon with the reviews are toilet, bad, treat, mosquito, sick,
lying, greedy, weird, dirt, pollution, messy, overpriced, and stinking. Most of
these words are related to cleanliness, health and hygiene. Some ill maintained
homestays, hosts being greedy & overcharging, unclean homestay premises
&locality may result in guests feeling disgusted. The words in the „fear‟
segment reflect unforeseen circumstances. „Sadness‟ segment words show that
visitors feel sad mainly when they are disappointed with the failure of or
mistake in trip plan. These altogether contribute for building up negative
sentiment.
Figure 3: Negative Sentiment Wordcloud
Positive sentiment exceeds negative sentiment by a huge margin (Figure 4).
Trust and joy are major contributor in building up positive sentiment towards
homestay suggesting that people find enjoyment in homestay and at the same
time they also feel safe. Sadness & fear contributes more in building up
negative sentiments which may have resulted due to unforeseen circumstances
and disappointment during visit.
Figure 4: Sentiment Plot
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Correlation Test
Homestay services in India altogether have some common attributes and despite
of different geographical locations with diverse culture. There are some
differential attributes based on the uniqueness of locality and culture. The
proportion of similarity and difference were analyzed using correlation between
the words used in online reviews of different homestays. The results of
correlation are presented below (Table 1). There is a strong, positive correlation
between the words in the reviews of homestays located in nine states, which is
statistically significant (p < .005).The word frequencies are positively correlated
between all states, correlation coefficient ranges from 0.7816 – 0.9413. Assam
and Uttar Pradesh word frequencies are less correlated with other states. One
possible reason for this may be less availability of Homestay reviews of these
states in the Trip advisor website which leads to less reserve of words.
However, overall results show that there is a huge commonality in words usage
in homestay review writing.
Table 1: Correlation Coefficient of Word Frequencies between Homestays
Assam
Himachal
Pradesh
Jammu &
Kashmir
Karnataka
Rajasthan
Sikkim
Kerala
Uttar
Pradesh
Uttarakhand
Assam
0.0000
Himachal
Pradesh
0.8048
0.0000
Jammu &
Kashmir
0.8170
0.9413
0.0000
Karnataka
0.8243
0.9347
0.9133
0.0000
Rajasthan
0.8181
0.8707
0.9113
0.8556
0.0000
Sikkim
0.8342
0.9076
0.8897
0.9078
0.8711
0.0000
Kerala
0.7981
0.9282
0.9407
0.9113
0.9114
0.9010
0.0000
Uttar Pradesh
0.8291
0.7816
0.8142
0.7931
0.8036
0.8638
0.8389
0.0000
Uttarakhand
0.7880
0.8405
0.8917
0.8556
0.8681
0.8891
0.8586
0.8063
0.0000
Table 2: Distribution of Extracted Reviews
State
Reviews
1
Assam
40
2
Himachal Pradesh
1075
3
Jammu & Kashmir
425
4
Karnataka
2505
5
Rajasthan
1630
6
Sikkim
534
7
Kerala
5855
8
Uttar Pradesh
1240
9
Uttarakhand
780
Total
14084
The existence of more common features than difference is exhibited by word
frequency comparison plot between Kerala and other 8 states (For example:
Figure 5). Words those are close to the line in these plots have similar
frequencies in both sets of texts. Words those are far from the line are words
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which are found more in one set of texts than another. For example, in the
review texts of Rajasthan and Kerala (Figure 5),high frequency correlated
words are „family‟, „breakfast‟, „clean‟, „amazing‟, „food‟, „bed‟, „birds‟,
„accessible‟, „air‟, „authentic‟, „accommodating‟, „attentive‟, „affection‟,
„affordable‟, „activity‟ etc. This indicates that guests often talk about:
facilities provided by homestay, behavior of the hosts, activities they can take
part and surrounding environment irrespective of homestay they stayed.
Further, it can be said that these are the attributes of their concern as the
contents of guest reviews are mostly about what matters to them during their
stay (Park & Allen, 2013). Words those show low correlations are more
specific to the reviews of particular homestay location. For example „camel‟,
„haveli‟, „hut‟, „safari‟ etc . are related to Rajasthan where tourists go for
desert safaris in camels. Many hosts in Rajasthan offer accommodation in
mud huts. „Haveli‟ is typical to Rajasthan meaning „Traditional space of
courtyard house‟ (Bryden, 2004). The words „coffee‟, „Ayurveda‟, „hills‟,
„boat‟, „good climate‟, gardens etc. relates to Kerala. This is suggestive of
homestays which are located in hilly areas having pleasant climate as well as
coffee & tea plantations and house boats available in backwaters. Kerala is
definitely a popular „Ayurveda‟ treatment hub (Kudlu, 2016). It is evident
from the frequency of words that attributes or facilities in homestays are of
more importance to the guests rather than attractions in the destination.
Figure 5: Comparing the Word Frequencies of Kerala and
Rajasthan Homestay Reviews
Figure 6: Comparing the Word Frequencies of Kerala, Assam and
Himachal Pradesh Homestay Reviews
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Figure 7: Comparing the Word Frequencies of Kerala, Jammu Kashmir and Karnataka
Homestay Reviews
Figure 8: Comparing the Word Frequencies of Kerala, Uttar Pradesh and Uttarakhand
Homestay Reviews
Figure 9: Comparing the Word Frequencies of Kerala and Sikkim
Homestay Reviews
5. Conclusion
This study examined the frequency of emotional words used by tourists in
their online reviews of homestays to explore the common and specific
attributes which are mostly spoken about. The most popular attributes in
online reviews are of utmost importance to tourists during their stay which
becomes the basis for others while selecting the accommodation. Findings of
the study indicate that warm welcome by hosts, excellent service with smile
and love, being helpful to the guests is among top attributes that guests talk
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about in reviews. This supports the similar findings of (Tussyadiah &
Pesonen, 2016) in which they found that „feeling being welcomed at
someone‟s house and „hospitality of hosts‟ are among top reasons that make
tourists choose homestay accommodation. Further, findings such as „home-
cooked delicious food‟ supports the findings of (Mura, 2015); „hygiene‟,
„comfort‟, „scenic beauty‟, „affordable price‟ are at par with findings of(Hsu
& Lin, 2011; Rasoolimanesh, Dahalan, & Jaafar, 2016); „authentic local
experience‟ or „authentic information to the guests‟ similar to the findings of
(Wang, 2007) that guests look for authentic experiences; „providing services
as per the requirement of guests‟, „unique local festivals‟, supports the
findings of (Mcintosh & Siggs, 2005)in which „personalized services‟ and
„unique feature of homestay‟ was found as important attributes that guests
look for in a homestay accommodation. „Being invited in local celebrations‟,
„interaction with locals‟, supports the findings of (Agyeiwaah E. ,
Akyeampong, Amenumey, & Boakye, 2014) in which they concluded that
guests choose homestay accommodation since they want to immerse
themselves in local culture. It was well understood from the reviews that
when guests find what they look for in homestays, it results in generating
positive emotions towards homestays. In the context of sentiment, overall
results show that positive sentiment towards the attributes of homestay
accommodation is way higher than negative sentiment. This is a new finding
as this study is first of its kind in which sentiment analysis on homestay
accommodation is done. High positive sentiment towards homestay supports
the logic behind consistent growth of homestay accommodation in the last
decade(Bhatt, 2012).Negative sentiments, though found very low, are usually
triggered by problems faced by guests during the stay. The findings of this
paper such as „unhygienic toilets‟, „mosquitos‟, „dirty surroundings‟ support
the findings of (Hamzah, 2008) in which „unhygienic conditions‟ in homestay
made guests feel disgusted. Further, „services not at par with high prices‟,
„slow response in problem handling, and „unnecessary disturbances, noisy
environment‟, poor accessibility were among the concerns expressed by some
guests in their reviews. Strikes and political unrest although not in control of
hosts, also caused disappointment among guests which contributed to
negative sentiments. These are new findings in the context of problems faced
by guests as there are limited researches available particularly on problems
faced by tourists/guests during the stay in a homestay accommodation.
6. Implications & Scope for Further Studies
In general, the study of customer generated information at attributes level
gives a lot of inputs to the service provider in designing and delivering the
service thereby one can reduce service quality gaps (Parasuraman, Zeithaml,
& Berry, 1985). The homestay attributes identified in the analysis of online
reviews can act as validation for existing items in homestay service
performance & satisfaction measuring scales. This work throws light on the
need of further empirical studies on the creation of various emotions in tourists
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by homestay service performance as well as impact of emotions on word of
mouth. To the homestay service providers, tourist generated online
information in the form of reviews brings an understanding about tourist
preferred attributes of homestay. These help them in designing better
promotional activities and enhance their services. In addition, sentiment
analysis of online tourist reviews can act as summary of service performance
evaluation by tourists. By considering this, service providers can take
measures to improve their service to ensure their sustainability in the
industry.
This work throws light on the need of further studies on the impact of various
sentiment emotions in enhancing customer relationship. The findings of this
study should be viewed in light of limitations before generalizing the results.
First, even though reviews gave rich information, all were extracted from a
single website. Second, reviews only relating to Indian Homestays were
taken. Third, reviewers may not be the expert of language and may have used
words and sentences as per their understanding and vocabulary. Further
studies using same approach can be conducted by taking reviews from
different websites as well as taking homestays from across nations. This
approach can also be extended to conduct similar studies on various other
services as well as products.
References
[1] Abreu J., Martins P.V., Fernandes, S., & Zacarias, M. (2013).
Business Processes Improvement on Maintenance
Management:. Conference on ENTERprise Information
Systems (pp. 320 – 330). Elsevier.
[2] Acharya, B. P., & Halpenny, E. A. (2013). Product for
Sustainable Community Development: A Case Study of
Women Managed Tourism Product in Rural Nepal. Tourism
Planning & Development, 367-387.
[3] Agyeiwaah, E. (2013). Volunteer tourists’ motivations for
choosing homestay in the Kumasi metropolis of Ghana. African
Journal of Hospitality, Tourism and Leisure, 2(3), 1-11.
[4] Agyeiwaah, E., Akyeampong, O., & Amenumey, E. K. (2014).
International tourists’ motivations to choose homestay: Do their
socio-demographics have any influence? Tourism and
Hospitality Research, 16-26.
[5] Agyeiwaah, E., Akyeampong, O., Amenumey, E., & Boakye, K.
A. (2014). Accommodation preference among international
volunteer tourists in the Kumasi Metropolis of Ghana. Tourism
Management Perspectives 10, 7–10.
[6] Agyeiwaah, E., Akyeampong, O., Boakye, K. A., & Adu-
Gwamfi, A. B. (2014). Pros and Cons of Hosting International
International Journal of Pure and Applied Mathematics Special Issue
1579

Tourists: Is It Rational? Tourism Planning & Development, 11
(4), 435–451.
[7] Andriotis, K., & Agiomirgianakis, G. (2013). Market escape
through exchange: home swap as a form of non-commercial
hospitality. Current Issues in Tourism, 17(7), 576-591.
[8] Bhalla, P., Coghlan, A., & Bhattacharya, P. (2016). Homestays'
contribution to community-based ecotourism in the Himalayan
region of India. Tourism Recreation Research, 41 (2), 213-228.
[9] Bhatt, S. (2012). Community-based Homestays: Innovation in
Tourism. Journal of the Development and Research
Organisation for Nature, Arts and Heritage, IX(2).
[10] Bryden, I. (2004). There is no outer without inner space’:
constructing the haveli as home. Cultural Geographies, 26-41.
[11] Chen, J., Sloan, P., & Legrand, W. (2010). Sustainability in the
Hospitality Industry. Routledge.
[12] Chen, Y., & Xie, J. (2008). Online Consumer Review: Word-of-
Mouth as a New Element of Marketing Communication Mix.
Management Science, 54(3), 477-491.
[13] Filieri, R., & McLeay, F. (2014). E-WOM and Accommodation:
An Analysis of the Factors That InfluenceTravelers’ Adoption of
Information fromOnline Reviews. Journal of Travel Research,
53(1), 44.
[14] Gan, S. W., Inversii, A., & Rega, I. (2018). Tourism,
Development and Digital Technologies: Insights from
Malaysian Homestays. In Information and Communication
Technologies in Tourism 2018 (pp. 52-63). Cham: Springer.
[15] Gössling, S., & Lane, B. (2014). Rural tourism and the
development of Internet-based accommodation booking
platforms: a study in the advantages,dangers and implications
of innovation. Journal of Sustainable Tourism, 23(8-9), 1386-
1403.
[16] Gretzel, U., & Yoo, K. H. (2008). Use and Impact of Online
Travel Reviews. In O. P., H. W., & G. U, Information and
Communication Technologies in Tourism 2008 (pp. 35-46).
Vienna: Springer.
[17] Gu, M., & Wong, P. P. (2006). Tourism Geographies: An
International Journal of Tourism Space, Place and
Environment. Tourism Geographies, 8(3), 253–273.
[18] Gunasekaran, N., & Anandkumar, V. (2012). Factors of
influence in choosing alternative accommodation:A study with
International Journal of Pure and Applied Mathematics Special Issue
1580

reference to Pondicherry, a coastal heritage town. Procedia
Social and Behavioral Sciences, 1127-1132.
[19] Guttentag, D. (2015). Airbnb: disruptive innovation and the rise
of an informal tourism accommodation sector. Current Issues in
Tourism, 18(12), 1192-1217.
[20] Hamzah, A. (2008). Malaysian homestays from the perspective
of young Japanese tourists: The quest for Furusato. In J.
Cochrane, Asian Tourism: Growth and Change (pp. 193-207).
Elsevier.
[21] Hasan, M. A., Sabtu, N. S., & Sahari, N. H. (2016). Syariah
Compliant Homestay in Malaysia: Formulating Legal
Framework. In A. M. S., A. R. F., & S. M (Eds.), Contemporary
Issues and Development in the Global Halal Industry (pp. 371-
380). Singapore: Springer.
[22] Hjulmand, L. G., Nielsen, U., Vesterlokke, P., Busk, R. J., &
Erichsen, E. (2003). TOURISM AS A DEVELOPMENT
STRATEGY IN RURAL AREAS ADJACENT TO THE
CROCKER RANGE NATIONAL PARK, SABAH, MALAYSIA.
ASEAN Review of Biodiversity and Environmental
Conservation (ARBEC).
[23] Hsu, S. L., & Lin, Y. M. (2011). Factors Underlying College
Students' choice of homestay accomodation while travelling.
World Transactions on Engineering and Technology Education,
196-202.
[24] Jamal, S. A. (2011). Tourist perceived value in a community-
based homestay visit: An investigation into the functional and
experiential aspect of value. Journal of Vacation Marketing,
17(1), 5-15.
[25] Jamal, S. A., Othman, N., & Muhammad, N. M. (2011). The
Moderating Influence of Psychographics in Homestay Tourism
in Malaysia. Journal of Travel & Tourism Marketing, 28(1), 48-
61.
[26] Kline, S. F., Morrison, A. M., & John, A. S. (2005). Exploring
Bed & Breakfast Websites. Journal of Travel & Tourism
Marketing, 17(2-3), 253-267.
[27] Kontogeorgopoulos, N., Churyen, A., & Duangsaeng, V.
(2015). Homestay Tourism and the Commercialization of the
Rural Home in Thailand. Asia Pacific Journal of Tourism
Research, 20(1), 29-50.
[28] Kudlu, C. (2016). Keeping the doctor in the loop: Ayurvedic
pharmaceuticals in Kerala. Anthropology & Medicine, 275-294.
International Journal of Pure and Applied Mathematics Special Issue
1581

[29] Labrecque, L. I., Esche, J. V., Mathwick, C., Novak, T. P., &
Hofacker, C. F. (2013). Consumer Power: Evolution in the
Digital Age. Journal of Interactive Marketing, 27, 257-269.
[30] Lanier, P., & Berman, J. (1993). Bed-and-breakfast inns come
of age. The Cornell Hotel and Restaurant Administration
Quarterly, 34(2), 15-23.
[31] Lee, H. A., Law, R., & Murphy, J. (2011). Helpful Reviewers in
TripAdvisor, an Online Travel Community. Journal of Travel
and Tourism Marketing, 28(7), 675-688.
[32] Liu, B. (n.d.). Opinion Mining, Sentiment Analysis, Opinion
Extraction. Retrieved from Cs.uic.edu:
https://www.cs.uic.edu/~liub/FBS/sentiment-
analysis.html#datasets
[33] Liu, Q. B., & Karahanna, E. (2017). The dark side of reviews:
The swaying effects of online product reviews on attribute
preference construction. MIS Quarterly, 41(2), 427-448.
[34] Mattila, A. S., & Ro, H. (2008). Discrete Negative Emotions and
Customer Dissatisfaction Responses in a Casual Restaurant
Setting. Journal of Hospitality & Tourism Research, 32(89).
[35] Mcintosh, A. J., & Siggs, A. (2005). An Exploration of the
Experiential Nature of Boutique Accomodation. Journal of
Travel Research, 74-81.
[36] Mohammad, S., & Turney, P. (2013). Crowdsourcing a Word-
Emotion Association Lexicon. Computational Intelligence,
29(3), 436-465.
[37] Molina-Azorín, J. F., Tarí, J. J., Pereira-Moline, J., Gamero, M.
D., & Pertusa-Ortega, E. M. (2015). The effects of quality and
environmental management on competitive advantage: A
mixed methods study in the hotel industry. Tourism
Management, 41-54.
[38] Mura, P. (2015). Perceptions of authenticity in a Malaysian
homestay e A narrative analysis. Tourism Management, 51,
225-233.
[39] O'Connor, P. (2008). User-Generated Content and Travel: A
Case Study on Tripadvisor.Com. Information and
Communication Technologies in Tourism, 47-58.
[40] Oguchaa, E. B., Riungub, G. K., Kiamaa, F. K., & Mukolwe, E.
(2015). The influence of homestay facilities on tourist
satisfaction in the Lake Victoria Kenya Tourism Circuit. Journal
of Ecotourism, 278-287.
International Journal of Pure and Applied Mathematics Special Issue
1582

[41] Park, S.-Y., & Allen, J. P. (2012). Responding to Online
Reviews: Problem Solving and Engagement in Hotels. Cornell
Hospitality Quarterly, 1–10.
[42] Park, S.-Y., & Allen, J. P. (2013). Responding to Online
Reviews: Problem Solving and Engagement in Hotels. Cornell
Hospitality Quarterly, 64-73.
[43] Pratap, R. (2016, May 13). A place like home. Retrieved from
businessline:
https://www.thehindubusinessline.com/blink/work/a-place-like-
home/article8590747.ece
[44] Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer
requirements from online reviews: A product improvement
perspective. Information and Management, 53(8), 951-963.
[45] Rahmani, K., Gnoth, J., & Mather, D. (2017). Tourists’
Participation on Web 2.0: A Corpus Linguistic Analysis of
Experiences. Journal of Travel Research, 1 –13.
[46] Rasoolimanesh, S. M., Dahalan, N., & Jaafar, M. (2016).
Tourists' perceived value and satisfaction in a community-
based homestay in the Lenggong Valley World Heritage Site.
Journal of Hospitality and Tourism Management, 72-81.
[47] Richins, M. L. (1983). Negative Word-of-Mouth by Dissatisfied
Consumers: A Pilot Study. Journal of Marketing, 47(1), 68-78.
[48] Robinson, J. (n.d.). Text Mining with R. Retrieved from
Tidytextmining.com:
https://www.tidytextmining.com/sentiment.html
[49] Samsudin, P. Y., & Maliki, N. Z. (2015). Preserving Cultural
Landscape in Homestay Programme Towards Sustainable
Tourism: Brief critical review concept. Asian Conference on
Environment-Behaviour Studies (pp. 433 – 441). Seoul:
Elsevier.
[50] Shan, S., Ren, J., & Li, C. (2016). The dynamic evolution of
social ties and user-generated content: a case study on a
Douban group. Enterprise Information Syetems, 1-19.
[51] Truong, V. D., Hall, C. M., & Garry, T. (2014). Tourism and
poverty alleviation:perceptions and experiences of poorpeople
in Sapa, Vietnam. Journal of Sustainable Tourism, 1071-1089.
[52] Tussyadiah, l. P., & Pesonen, J. (2016). Impacts of peer-to-
peer accommodation use on travel patterns. Journal of Travel
Research, 55(8), 1022-1040.
[53] Wang, Y. (2007). Customised Authenticity begins at home.
Annals of Tourism Research, 34(3), 789-804.
International Journal of Pure and Applied Mathematics Special Issue
1583

[54] Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of
user-generated content on traveler behavior: An empirical
investigation on the effects of e-word-of-mouth to hotel online
bookings. Computers in Human Behavior, 27(2), 634-639.
[55] Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and
conventional media on firm equity value: A sentiment analysis
Approach. Decision Support Systems, 919-926.
International Journal of Pure and Applied Mathematics Special Issue
1584

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