Understanding Visitor Experience Interactions at Cultural Heritage Sites: A Text Analytics Approach

Understanding Visitor Experience Interactions at 

Cultural Heritage Sites: A Text Analytics Approach

Andi Smart, Pikakshi Manchanda 

University of Exeter Business School, Exeter, UK and

Gaël Bonnin, Valérie Duthoit – Saint Georges  NEOMA Business School, France



This paper focuses on the discovery of a domain-specific schema to analyse visitor experience in cultural-heritage tourism. Natural Language Processing (NLP) to determine experience interactions (and associated sentiment) is used to construct the schema. A domain-specific schema provides a research framework that permits intersite comparison and the pursuit of experience quality. The schema is therefore useful for both practitioners and researchers and provides distinct opportunities for theoretical development within tourism research.


Cultural Tourism, Visitor Experience, Natural Language Processing, Sentiment Analysis, Service Design


The tourism industry has seen a considerable rise in user-generated feedback on online platforms. While there has been a growing body of research in the tourism and hospitality domain using NLP approaches such as sentiment analysis (Berezina et al., 2016; Marrese-Taylor et al., 2014; Valdivia et al., 2017), many of these studies are not able to capture or evaluate domain-specific features in tourist feedback. In particular, this has not been undertaken in a cultural-heritage context focused on determining visitor experience. Our research is focused on the discovery of domain-specific features present in experience-driven visitor feedback in cultural-heritage tourism. Specifically, we focus on identification and classification of experience interactions to inform a schema. This schema is subsequently used to evaluate the interactions using aspect-level sentiment analysis. Such an approach is used to understand visitor preferences, motivations and interactions at cultural heritage sites. Our investigation builds upon a synthesis of previous studies that have focused on the evaluation of customer experience (McColl-Kennedy et al., 2019; Ordenes et al., 2014; Ponsignon et al., 2015, 2017).

Identifying specific instances of visitor interaction (LaSalle & Britton, 2002; Wemmerlöv, 1990) within unstructured data, and determining the associated sentiment provides distinct opportunities for curators and managers. Site managers cannot design visitor experiences (Ponsignon et al., 2017), but can only design service systems through which visitors derive value (Roth & Menor, 2003). Customers evaluate their overall experience through the combinatorial effect of their interactions with the resources made available within the service system (Lemke et al., 2011; Meyer & Schwager, 2007). The identification of these interactions therefore provides opportunities to review curation and broader system design in pursuit of heightened visitor experience. Furthermore, attaining high levels of visitor experience impacts upon desirable outcomes such as improved economic value (Pine & Gilmore, 1998), visitor satisfaction (Liljander & Strandvik, 1997), positive recommendation and loyalty (Homburg et al., 2017; Meyer & Schwager, 2007; Stein & Ramaseshan, 2016).


Johnston & Kong (2011) define customer experience as “personal interpretation of the service process and their interaction and involvement with it during their journey through a series of touchpoints, and how those things make the customers feel”. While experiences cannot be directly designed, they can be impacted through the careful consideration of touchpoints that the customer (visitor) engages with while within the service system (Tussyadiah, 2014).


As part of our ongoing investigations, we are working with heritage sites in England and France to analyse visitor interactions using NLP techniques. For this paper, we consider the case study of an Anglican Cathedral in England. A total of 2293 unsolicited textual visitor reviews were collected from TripAdvisor (November 2002 to February 2019). In addition to reviews, a review date and a visitor rating (1-5) were also obtained. Figure 1 shows the rating distribution for the dataset.

Figure 1: Rating Distribution

We have observed that datasets for religious heritage sites such as cathedrals tend to be biased towards higher visitor ratings. To investigate these ratings further we conducted a review-level sentiment analysis to determine a polarity score for each review (-1.0 to 1.0).

Figure 2: Histogram of Sentiment Polarity

The polarity distribution (Figure 2) provides an alternative analytical perspective that suggests that the sentiment within the natural language corroborates the bias in the rating. However, further analysis of the correlation between rating and sentiment polarity (Figure 3) reveals some negative sentiment across all ratings. We suggest that it is this feedback which is most beneficial to cultural heritage sites who are pursuing exceptional experiences for their visitors.

Figure 3: Strong Positive Correlation: Ratings and Sentiment Polarity

It is difficult, however, to determine the specificity of the outliers without drilling down into the dataset. To facilitate this analysis we constructed a schema comprised of six core categories that represent experience interactions, price and external elements:

  • Site Offerings: Refers to overall site space and premises, in association with physical content (such as art and artefacts) and abstract details (such as site aesthetics).
  • Interpretation Offerings: Refers to tools or sources available at a heritage site to aid in understanding of the cultural offerings. This includes digital and non-digital interpretation tools such as audio guides, instruction manuals etc.
  • Supporting Services: Refers to services and provisions available at a heritage site to aid in understanding of the cultural offerings. This includes tourist interaction points, and provisions for disabled visitors, parking, communication and subsistence/recreation.
  • Staff: Refers to mediation and non-mediation site personnel such as tour guides, volunteers etc.
  • Price: Refers to service or product pricing options available at a heritage site, such as discounts, incentives, ticket costs etc.
  • External Elements: Refers to elements independent of the site offering that have an impact on the visitor’s experience. This includes elements such as external climate, site upkeep, transportation facilities etc.

Using text mining and analytics, we identified 2673 unique textual concepts to determine a concept library. A frequency distribution indicating the instances of these concepts within each category is shown in Figure 4.

Figure 4: Frequency Distribution of Key Categories

Additionally, we conducted an aspect-level sentiment analysis with regards to the key categories of the domain-specific schema (Figure 5). During our preliminary investigation, we discovered that visitors expressed a negative sentiment regarding ‘price’ (service costs of the cathedral). Furthermore, the analysis revealed that of the 28.61% reviews with instances referring to the ‘staff’ category, 10.06% represented disquiet with staff attitude and behaviour. The schema, and associated aspectlevel sentiment, provides a useful approach to determine improvement opportunities independent of standard rating systems that obfuscate this detail.

Figure 5: Sentiment Distribution of Key Categories


The dramatic rise in visitor feedback presents opportunities to understand experience interactions of visitors. The proposed NLP approach reveals insights that are obscured by standard rating systems. A domain-specific schema has been created to permit aspect-level sentiment analysis to reveal these visitor insights. The proposed approach may be used for detailed evaluation of visitor feedback and inter-site comparison with respect to visitor experiences.



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