Cruise vacation experiences for Chinese families with young children

Wang, Sha; Chu, Tianyun; Li, Huiying; Sun, Qiang

doi:10.1108/tr-08-2021-0394

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…In addition, there are various approaches to calculate whether the sentiment is positive, negative, or neutral, e.g. a study that explored the cruise experiences (Wang et al , 2022) considered a comment as negative “if a comment's positive score was less than or equal to two times the absolute value of its negative score”. Moreover, a recent survey on the sentiment analysis in hospitality and tourism (Mehraliyev et al , 2022) has reported that the studies that test the performance of sentiment analysis are rare, thus our results contribute to filling this gap.…”

Section: Resultsmentioning

confidence: 99%

“…In the comparison of our results to the results of others (Gitto and Mancuso, 2017;Mehta et al, 2021;Wang et al, 2022), it can be noted that there are differences in the size, quality, and purpose of a particular dataset and different uses and implementations of sentiment analysis. In addition, there are various approaches to calculate whether the sentiment is positive, negative, or neutral, e.g.…”

Section: Analyzing Tourist Reviews Using Machine Learningmentioning

confidence: 86%

“…Additional features may also be learned from the text, e.g. by content analysis (Wang et al , 2022), or topic analysis (Gour et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Additional features may also be learned from the text, e.g. by content analysis (Wang et al, 2022), or topic analysis (Gour et al, 2021). JHTI Furthermore, the touristic insights made from these models may provide a basis for the understanding of tourist behavior patterns and setting up a theoretical framework for shaping public opinion, i.e.…”

Section: Theoretical Implicationsmentioning

confidence: 99%

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Predicting sentiment and rating of tourist reviews using machine learning

Puh

Babac

2022

JHTI

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PurposeAs the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.Design/methodology/approachThis paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.FindingsThe performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.Practical implicationsThe proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.Originality/valueThis study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

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Section: Resultsmentioning

confidence: 99%

Section: Analyzing Tourist Reviews Using Machine Learningmentioning

confidence: 86%

“…Additional features may also be learned from the text, e.g. by content analysis (Wang et al , 2022), or topic analysis (Gour et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

Section: Theoretical Implicationsmentioning

confidence: 99%

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Predicting sentiment and rating of tourist reviews using machine learning

Puh

Babac

2022

JHTI

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Millennials’ interactions with social media content on cruise tourism

Kim,

Lei

2024

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Purpose The cruise industry has used technology to attract more millennials than ever before. The progression of social media has transformed the way young individuals gather information for their travel decision-making. Hence, this study aims to investigate the effects of the characteristics of social media – Instagram – on millennials’ trust in and attitudes toward social media content and their behavioral intentions. Design/methodology/approach A self-administered survey was designed to test the study model, and 323 responses collected were deemed valid for main data analysis. Findings The results revealed that perceived enjoyment of content is a powerful antecedent of trust, attitudes and behavioral intentions. Content quality is a strong predictor of trust but has no meaningful effect on attitudes and behavioral intentions. Originality/value Theoretically, this study contributes to the research in the tourism literature on social media and cruise marketing based on integrating into the technology acceptance model, the characteristics of social media content and trust based on commitment-trust theory. This study can help cruise operators use Instagram to influence millennials and suggests significant implications based on social media interactions with millennials.

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Cruise tourism research: a horizon 2050 paper

Papathanassis

2024

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Purpose This study aims to empower cruise academia to assert their role as enablers, and potentially drivers, of sustainable, responsible and resilient cruise futures. Design/methodology/approach This contribution represents a synthesis and a critical reflection of cruise-related research-review literature over the last four decades, assessing its impact and potential in terms of influencing the evolution and future developments of cruise tourism. Findings Cruise research between the 1980s and the first decade of this century could be characterised as “ship-centric” and managerially focused. Over the last decade and up to the aftermath of the COVID-19 pandemic, cruise research has become increasingly “cruise-destination centric”, with sustainability emerging as a dominant theme. Diverging somewhat from the public and media emphasis on environmental concerns and risks, academic research tends to focus on the economic aspects of sustainability. Nonetheless, and despite aiming for practical relevance, cruise research tends to remain reactive and lags behind industry trends. Research limitations/implications This paper proposes a shift from a “applied” to an “applicable” research paradigm, inspiring researchers to proactively shape the industry‘s future by embracing “what-ifs” in their thematic scope. Such a paradigm calls for a normativity-enriched methodological diversity, as well as the inclusion of transformational relevance as an indicator of research impact. Originality/value Public perceptions of cruise tourism have changed over the years, evolving from that of a promising holiday niche to a symbol of unsustainable mass tourism. In this context, cruise scholars are offered guidance for transcending the polarising debate of growth versus de-growth/limits while increasing the relevance and transformational impact of cruise scholarship.

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Cruise vacation experiences for Chinese families with young children

Cited by 3 publications

References 43 publications

Predicting sentiment and rating of tourist reviews using machine learning

Predicting sentiment and rating of tourist reviews using machine learning

Millennials’ interactions with social media content on cruise tourism

Cruise tourism research: a horizon 2050 paper

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