Evaluating the Effect of Weather on Tourist Revisit Intention using Natural Language Processing and Classification Techniques
Date Issued
October 2021
Abstract
Tourists’ revisit has significant monetary benefits
to destinations because the cost of retaining existing visitors is
less than attracting new visitors. Re-visit intention is often based
on tourists experience and satisfaction at a destination. An
important aspect that influences the relationship between
satisfaction and intention to revisit is the weather conditions at
a destination given the increased frequency of heatwaves that
strike summer holiday destinations over the summer months.
This work applies natural language processing and
classification techniques to evaluate the impact of weather
information on revisit intention utilizing reviews from
TripAdvisor and online weather data. Information retrieval
techniques (Doc2Vec) are applied on online reviews collected
during the summer months between 2010-2019 from tourists
that visited Cyprus. Reviews are labeled as “revisits” or
“neutral” based on their textual content. The labelled reviews
dataset is enhanced with weather information based on the
reviews’ timestamp, such as temperature and humidity of
tourists’ country of origin and Cyprus at the time of the visit to
the hotel/destination. To account for the influence of hotel
infrastructure and available services to deal with heatwaves
(i.e., climate-controlled), the training dataset included hotel star
rating as an additional parameter. An ensemble gradient
boosting tree classifier is trained utilizing the compiled dataset
to predict revisit intention. The classifier is evaluated against the
area under the curve. To interpret the classifier’s inherent
patterns, a popular machine learning interpretation technique
is used, namely Shapley Additive Explanation (SHAP).
Visualizations of the model using SHAP indicate that the heat
index and weather difference between destination and country
of origin influence revisit intention. Such preliminary insights
are encouraging for further investigations with an end goal to
develop a decision support system to assist destination managers
during their target marketing campaigns.
to destinations because the cost of retaining existing visitors is
less than attracting new visitors. Re-visit intention is often based
on tourists experience and satisfaction at a destination. An
important aspect that influences the relationship between
satisfaction and intention to revisit is the weather conditions at
a destination given the increased frequency of heatwaves that
strike summer holiday destinations over the summer months.
This work applies natural language processing and
classification techniques to evaluate the impact of weather
information on revisit intention utilizing reviews from
TripAdvisor and online weather data. Information retrieval
techniques (Doc2Vec) are applied on online reviews collected
during the summer months between 2010-2019 from tourists
that visited Cyprus. Reviews are labeled as “revisits” or
“neutral” based on their textual content. The labelled reviews
dataset is enhanced with weather information based on the
reviews’ timestamp, such as temperature and humidity of
tourists’ country of origin and Cyprus at the time of the visit to
the hotel/destination. To account for the influence of hotel
infrastructure and available services to deal with heatwaves
(i.e., climate-controlled), the training dataset included hotel star
rating as an additional parameter. An ensemble gradient
boosting tree classifier is trained utilizing the compiled dataset
to predict revisit intention. The classifier is evaluated against the
area under the curve. To interpret the classifier’s inherent
patterns, a popular machine learning interpretation technique
is used, namely Shapley Additive Explanation (SHAP).
Visualizations of the model using SHAP indicate that the heat
index and weather difference between destination and country
of origin influence revisit intention. Such preliminary insights
are encouraging for further investigations with an end goal to
develop a decision support system to assist destination managers
during their target marketing campaigns.
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2021-SMC-WeatherTouristRevisit.pdf
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