Tourist Experiences at Overcrowded Attractions: A Text Analytics Approach

Yu, Chunxia; Egger, Roman

doi:10.1007/978-3-030-65785-7_21

Cited by 16 publications

(6 citation statements)

References 41 publications

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“…We have also shown that purpose-built AR experiences can be effective with very little in the way of a physical set (see Figure 4). Pragmatically, this means that tourism managers can host engaging experiences without the need for visitors to gather around delicate artifacts, thereby reducing overcrowding, which has been associated with several negative outcomes (e.g., safety concerns; Yu & Egger, 2021).…”

Section: Discussionmentioning

confidence: 99%

A Magic Leap in Tourism: Intended and Realized Experience of Head-Mounted Augmented Reality in a Museum Context

Bird

Smart

Harris

et al. 2022

Journal of Travel Research

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Augmented reality (AR) is an emergent technology in tourism. However, research concerning the AR user experience is relatively scarce and seldom addresses the intentions of designers. Accordingly, we sought to: (a) explore the design intentions underlying a multi-user, purpose-built AR experience; (b) assess the extent to which users’ realized experience aligned with the designers’ intended experience; and (c) examine the relationships between users’ internal states and their associated behavior, in alignment with a Stimulus-Organism-Response framework. In Study 1, designers ( n = 5) took part in a focus group and completed a design intentions survey. In Study 2, users ( n = 48) tested the AR experience, and a range of subjective (e.g., affective responses) and objective (i.e., visual attention) data were recorded. Findings indicated designer–user disparities primarily at the organism and response levels. Additionally, users’ affective responses to the AR experience were strongly associated with visitor engagement.

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Section: Discussionmentioning

confidence: 99%

A Magic Leap in Tourism: Intended and Realized Experience of Head-Mounted Augmented Reality in a Museum Context

Bird

Smart

Harris

et al. 2022

Journal of Travel Research

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“…Meanwhile, in many cases, insufficient information exists in tourism concerning the necessary steps of data pre-processing and how hyperparameters are defined. For instance, in some recent tourism studies, the default hyperparameters were used, and the number of topics was defined without estimating these parameters (Celuch, 2021; Yu and Egger, 2021). Consequently, non-ideal results might have been reported as a successful topic extraction.…”

Section: Discussionmentioning

confidence: 99%

Identifying hidden semantic structures in Instagram data: a topic modelling comparison

Egger

2021

Self Cite

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Purpose Intrigued by the methodological challenges emerging from text complexity, the purpose of this study is to evaluate the effectiveness of different topic modelling algorithms based on Instagram textual data. Design/methodology/approach By taking Instagram posts captioned with #darktourism as the study context, this research applies latent Dirichlet allocation (LDA), correlation explanation (CorEx), and non-negative matrix factorisation (NMF) to uncover tourist experiences. Findings CorEx outperforms LDA and NMF by classifying emerging dark sites and activities into 17 distinct topics. The results of LDA appear homogeneous and overlapping, whereas the extracted topics of NMF are not specific enough to gain deep insights. Originality/value This study assesses different topic modelling algorithms for knowledge extraction in the highly heterogeneous tourism industry. The findings unfold the complexity of analysing short-text social media data and strengthen the use of CorEx in analysing Instagram content.

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“…Xu et al [9] constructed an extended sentiment dictionary, including basic sentiment words, the field sentiment words and polysemy sentiment words, and obtained the sentiment polarity of the text by using the extended sentiment dictionary and the designed sentiment scoring rules. Yu and Egger [10] used the VADER algorithm, a sentiment-scoring method for text based on sentiment lexicons and rules, to explore how tourists feel about safety, service, queues, etc. when visiting popular and crowded attractions.…”

Section: A Sentiment Analysis Based On Sentiment Dictionarymentioning

confidence: 99%

A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

Hao

Xiao

2022

IEEE Access

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The most commonly used methods in text sentiment analysis are rule-based sentiment dictionary and machine learning, with the later referring to the use of vectors to represent text followed by the use of machine learning to classify the vectors. Both methods have their limitations, including inflexibility of rules, non-prominence of sentiment words. In this paper, we design a weight distributing method combining the two methods for text sentiment analysis, by which the sentence vectors obtained can both highlight words with sentiment meanings while retaining their text information. Empirical results show that based on this new method, the accuracy rate of text sentiment analysis can reach as high as 82.1%, which means 13.9% higher than rule-based sentiment dictionary method, and 7.7% higher than TF-IDF weighting method.

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Tourist Experiences at Overcrowded Attractions: A Text Analytics Approach

Cited by 16 publications

References 41 publications

A Magic Leap in Tourism: Intended and Realized Experience of Head-Mounted Augmented Reality in a Museum Context

A Magic Leap in Tourism: Intended and Realized Experience of Head-Mounted Augmented Reality in a Museum Context

Identifying hidden semantic structures in Instagram data: a topic modelling comparison

A Study of the Application of Weight Distributing Method Combining Sentiment Dictionary and TF-IDF for Text Sentiment Analysis

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