Text mining of tourism preference in a multilingual site

Zeng, Chao; Nakatoh, Tetsuya; Hirokawa, Sachio; Eguchi, Masanari

doi:10.1002/tee.22841

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Usually, such reviews are labelled as positive, negative, or neutral. Several countries implement such eCRM, by automatic classification of the reviews: China [12], the Sultanate of Oman [9], Serbia [13], Spain [14,15] and Japan [16], among others. Reviews are also used for several purposes, such as better understand of tourist attractions [17] and facilitation of service improvement.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

et al. 2021

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In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing activities, tools and techniques, delivered with the use of electronic channels for the specific purpose of locating, building and improving long- term relationships with customers, to enhance their individual potential. In this paper, we refer to the analysis of information in three aspects: customer satisfaction, the study of customer behavior and the forecast of tourist demand. Specifically, we have created a novel dataset comprising the non-verbal preference assessment of tourists who are clients of the Sol Cayo Guillermo hotel belonging to the Melia hotel chain, in Jardines del Rey, Cuba. Then, by applying Computational Intelligence algorithms to this dataset, we achieve segment customers according to their non-verbal preferences, in order to increase their satisfaction, and therefore the client profitability. In order to achieve a good performance in the realization of this task, we have proposed two modifications of the Naïve Associative Classifier, whose results are compared with the most relevant computational algorithms of the state of the art. The experimentally obtained values of balanced accuracy and averaged F1 measure show that, by clearly improving the results of the state-of-the-art algorithms, our proposal is adequate to successfully use electronic customer relationship management in the tourist services provided by hotel chains.

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Section: Related Workmentioning

confidence: 99%

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

et al. 2021

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“…This theme is very active at present, having been applied in countries such as China [71], the Sultanate of Oman [68], Serbia [72], Spain [73,74], among others. Text mining has also been used to establish tourist preferences in Japan [75].…”

Section: Previous Workmentioning

confidence: 99%

Electronic Customer Relationship Management in action: an example in business

2020

IJETER

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Nowadays, Electronic Customer Relationship Management (eCRM) constitutes a key aspect for management in the tourism and business industry. The automatic analysis of the available information is a major part of eCRM. In this paper, we address the use of eCRM in the development of tourism and business, and we consider a case of study to determine the usefulness of computational intelligence algorithms. We find that computational intelligence algorithms are very accurate for the determination of Online Shoppers Purchasing Intention, and can be easily applied for marketing purposes.

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“…This theme is currently very active, having been applied in countries such as China [6], the Sultanate of Oman [3], Serbia [7], Spain [8,9] among others. Text mining has also been used to establish tourist preferences in Japan [10]. This paper is organized as follows: section II analyzes some of the current research about eCRM in tourism, section III offers some works on automatic data analysis and applications for tourism, and section IV offers the conclusions.…”

Section: Introductionmentioning

confidence: 99%

Application of Data Engineering in Automatic information analysis for Electronic Customer Relationship Management: A Survey

2020

IJETER

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In this paper we address Customer Relationship Management and electronic Customer Relationship Management from the perspective of the confluence of two relevant areas of engineering: Behavioral Engineering and Data Engineering. After conducting extensive documentary research on the state of the art,we review some of the most recent works of automatic data analysis applied to the tourism industry, considering the particularities of Behavioral Engineering within the systemic paradigm. As a result of this research work, one of the most important conclusions we have reached is the following: we determine that, to obtain an efficient electronic Customer Relationship Management model in a tourism related organization, the use of Intelligent Computing techniques for automatic data analysis is needed to ostensibly improve working conditions related to the tourism industry.

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Text mining of tourism preference in a multilingual site

Cited by 6 publications

References 11 publications

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence

Electronic Customer Relationship Management in action: an example in business

Application of Data Engineering in Automatic information analysis for Electronic Customer Relationship Management: A Survey

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