Lake-Destination Image Attributes: Content Analysis of Text and Pictures

Rodrigues, Ana Isabel; Correia, Antónia; Kozak, Metin; Tuohino, Anja

doi:10.1108/s1871-317320150000010022

Cited by 20 publications

(23 citation statements)

References 39 publications

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“…Furthermore, this work contributes to expanding the research on destination image applied to lake tourism context. It allows the identification of comprehensive set of items that may be adopted to assess the lake destination image (Rodrigues et al, 2015).…”

Section: Theoretical Conclusionmentioning

confidence: 99%

Tourism and water resources: interrelationships and consequences. The case of Kazakhstan

Ramazanova¹

2020

EJTR

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Section: Theoretical Conclusionmentioning

confidence: 99%

Tourism and water resources: interrelationships and consequences. The case of Kazakhstan

Ramazanova¹

2020

EJTR

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“…Customer satisfaction is determined by a combination of customer expectations, perceived quality, and value; it has a direct effect on customer loyalty, which is essential for destinations in gaining competitive advantage [156,157]. Besides, because the determinants of tourist satisfaction may be different for different destinations, it is also a feasible method for studying the constituent variables of the image and their weights [23] to promote the evaluation of destination image. are important for the analysis of customer satisfaction.…”

Section: Market Supervisionmentioning

confidence: 99%

“…How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36].…”

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confidence: 99%

A Review of Text Corpus-Based Tourism Big Data Mining

Zhang

et al. 2019

Applied Sciences

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With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists' opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years. Appl. Sci. 2019, 9, 3300 2 of 27 the travel. In order to utilize this user-generated content properly and further to meet the needs of tourists and promote the tourism industry, we need to analyze and exploit tourists' needs and opinions, and then identify the problems of tourism services or destinations, which has become a new path for tourism development. Besides, as tourism needs become increasingly personalized, visitors begin to pursue self-likeness, self-worth, and diversified travel experiences, and they are no longer willing to endure delays or waits. How to recognize and respond to visitors' behaviors and needs quickly and identify potential customers have become essential factors for the success of tourism stakeholders. By exploiting the subjective information contained in tourism text data, we can assist tourism stakeholders to provide better services for tourists.A large number of text mining techniques have been proposed and applied to tourism text data analysis for creating tourist profiles [8][9][10][11][12][13][14][15] and making effective market supervision [16][17][18][19][20][21][22][23][24][25]. These approaches exploit a variety of text representation strategies [26][27][28][29][30][31][32] and use different NLP techniques for topic extraction [33], text classification [34], sentiment analysis [35], and text clustering [36]. Moreover, while aiming to make computers understand human language, NLP has become the essential tool for text data analysis and is undergoing fast-pace growing based on the applications of deep learning in word embedding, syntax analysis, machine translation, and text understanding. Machine learning-based NLP techniques have been widely used in tourism text analysis, with superior results [19,25]. In addition, due to its high capability for extracting selective and invariant features from texts, and its independency of prior knowledge and linguistic resources, deep learning has been reported to achieve higher performance than other approaches on many NL...

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“…Nowadays tourist resort areas tend to be situated in or near the water-based attractive areas. According to Gladstone et al (2012) coastal areas are the most attractive for tourists, thus during the last decades several concepts such as marine, coastal, river, and more recently lake tourism has been discussed in tourism literature (Hall & Härkönen, 2006;Rodrigues et al, 2015). Often, lakes are the main attraction for many destinations: Lake Balaton for Hungary, the Great Lakes in the USA, Lombardian lakes in Northern Italy, Finnish lakeland, Lake District in the UK, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of tourism development on surface area of main lakes of Shchuchinsk-Burabay resort area, Kazakhstan

Ramazanova

Bulai

Ursu

et al. 2019

EJTR

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Lake (or lacustrine) water is one of the most sensitive and vulnerable resources for many tourist destinations, whose attractiveness highly depends on the water quality and availability in the lakes. The Shchuchinsk-Burabay resort area, located in the Akmola region of the Northern Kazakhstan, is one of the most popular tourist destinations of the country, known for its attractive natural landscape and for its great number of lakes. However, during the last decades, these lakes have been facing environmental issues due to their overexploitation and increasing level of pollution. Thus, the aim of the current study is to examine the changes in surface area of these lakes during the last 30 years by using GIS technologies, and the influence of tourism industry upon this phenomenon. Accommodation facilities (hotels, sanatoriums and guesthouses), as main tourism-related water consumers, have been targeted within our analysis. Results show that tourism is one of the factors responsible for surface area decline of the main lakes in the region, and its responsibility increases when combined with other factors. This study also formulates recommendations for water management policymakers in order to guarantee the sustainability of lake water as a key resource of the tourism industry in the region.

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Lake-Destination Image Attributes: Content Analysis of Text and Pictures

Cited by 20 publications

References 39 publications

Tourism and water resources: interrelationships and consequences. The case of Kazakhstan

Tourism and water resources: interrelationships and consequences. The case of Kazakhstan

A Review of Text Corpus-Based Tourism Big Data Mining

Effects of tourism development on surface area of main lakes of Shchuchinsk-Burabay resort area, Kazakhstan

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