Exploring the characteristics of tourism industry by analyzing consumer review contents from social media: a case study of Bamako, Mali

Bruno, Sanogo; Yang, Chao; Tian, Wenyan; Xie, Zhong; Shao, Yuanzheng

doi:10.1080/10095020.2019.1649848

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…The problem with this classifier is that it needs the data to be balanced for it to predict the classes accurately. SVM is suitable to be used in tourism domain as it shows a high accuracy of 80.11% by using tourism dataset [24].…”

Section: Classification Methodsmentioning

confidence: 99%

“…Sentiment analysis is widely used in many domains such as education, healthcare, politics, e-commerce and many more [20] [21] [22] [23]. The sentiment analysis method is applied to one of the famous domains, tourist reviews, to understand tourists' experiences, opinions, and emotions towards a tourism destination [24] [25] It is better than the traditional way of sending questionnaires to get the visitors' feedback [25] [26].…”

Section: Sentiment Analysismentioning

confidence: 99%

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Sentiment classification from reviews for tourism analytics

Haris

Mutalib

Malik

et al. 2023

Int. J. Adv. Intell. Informatics

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User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain.

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Section: Classification Methodsmentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

Sentiment classification from reviews for tourism analytics

Haris

Mutalib

Malik

et al. 2023

Int. J. Adv. Intell. Informatics

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“…From their experiences of a place, visitors generate and upload information to online sites, which can help destinations understand visitor expectations and their image of the destination (Bruno et al 2019;Dediu 2016;Ye & Tussyadiah 2011). Online reviews empower potential visitors as they clarify information about a place, thus dealing with unrealistic expectations (Ye & Tussyadiah 2011).…”

Section: Value Of Online Reviewsmentioning

confidence: 99%

Placemaking and visitors’ reviews of the Golden Mile of Durban

Hlengwa

2021

Acta commer.

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Orientation: Placemaking is a proclivity of cities to change space into place through zoning, naming and development into attractive, people-friendly landscapes where diverse, harmonious and sometimes contradicting amenities are coalesced to attract people.Research purpose: To establish the perceptions of the visitors on appeal, experience and safety of the Golden Mile of Durban.Motivation for the study: The study was motivated by availability of online reviews that remained unanalysed and did not aid decision-making.Research design, approach and method: Data were collected from 287 reviews sampled from the Golden Mile website. Qualitative analysis was performed on the data and categorised according to appeal, experience and safety associated with the Golden Mile.Main findings: The study found that placemaking is always work in progress as destinations strive for competitiveness and to avoid obsoletion. An overwhelming majority of visitors rated the Golden Mile as good to excellent on appeal, experience and safety. Some criticised the place as unsafe with a number of dilapidated buildings spoiling its appeal.Practical/managerial implications: Planners and tourism developers should factor the grass root approach to placemaking by increasing security and urging property owners to revamp their buildings, thus keeping with the image of the place.Contribution/value-add: The article emphasises the significance and value added by online visitors’ reviews in placemaking.

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“…The authors note that while this kind of opinion mining is popular in business, it is less popular in government organizations, such as transport authorities in Indonesia. Bruno et al (2019) use data mining techniques to understand the tourism industry and travel behavior in Bamako, Mali. Mendez et al (2019) engaged in both sentiment analysis and topic modeling in order to capture bus user experiences on the network in Santiago, Chile.…”

Section: Related Workmentioning

confidence: 99%