Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

Chen, Yanyan; Zhong, Yumei; Yu, Su-min; Xiao, Yan; Chen, Sining

doi:10.3390/app12020692

Cited by 29 publications

(13 citation statements)

References 99 publications

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“…Satisfiers: 0.1<IA≤0.4 (10) Delighters: IA>0.4 (11) As depicted in Table 1, it is clear that seven out of ten attributes have significantly changed in impactasymmetry after experiencing the pandemic. Only three "Satisfiers" attributes, "service", "food," and "staff," were assessed as having no significant change across the three stages of the COVID-19 pandemic.…”

Section: Resultsmentioning

confidence: 99%

“…While the three-factor theory has been accepted and applied in various travel fields, some studies suggest that the degree to which an attribute affects customer satisfaction may vary in different contexts [10] . Chen et al (2022) found that hotel attributes contributing to high/low customer ratings differed among hotels with different star ratings [11] .…”

Section: The Asymmetric Impacts Of Hospitality Services On Customer S...mentioning

confidence: 99%

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Evolving Impacts of Attribute Performance on Customer Satisfaction across the Covid-19 Pandemic: An Empirical Case Study in Hospitality

2024

AJBM

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Previous literature has widely reported that the impact of service performance on customer satisfaction (CS) is asymmetrical. However, little is known about how the asymmetry of a service attribute changes at different usages. The tourism industry is highly vulnerable to pandemics. Numerous pieces of evidence show that customers' expectations of hospitality services have varied since the outbreak of COVID-19. It is reasonable to suppose that the asymmetry of a service attribute impacting CS evolves across pandemics. Based on scrutinizing 521,030 hotel reviews, this study investigates the differences in customer expectations and perceptions towards hospitality services before, within, and after the COVID-19 pandemic and uncovers the evolution of the impact mechanism of service performance on CS across time series. This study broadens existing asymmetry literature from a novel evolving perspective; the findings can help guide hoteliers in adjusting their service priorities according to the trend of pandemics.

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Section: Resultsmentioning

confidence: 99%

Section: The Asymmetric Impacts Of Hospitality Services On Customer S...mentioning

confidence: 99%

Evolving Impacts of Attribute Performance on Customer Satisfaction across the Covid-19 Pandemic: An Empirical Case Study in Hospitality

2024

AJBM

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“…The proposed methodology comprises four main components: data acquisition, preprocessing, classification, and evaluation. For sentiment analysis, different forms of data are examined by the research community, including Tweets [ 10 – 14 ], movie reviews [ 15 , 20 , 38 ], Amazon product reviews [ 23 , 32 ], TripAdvisor Reviews [ 45 ], Short Messages [ 43 ], and Hotel Reviews [ 53 ]. This research methodology considered a renowned dataset of tweets for detailed experimentation and evaluation.…”

Section: Methodsmentioning

confidence: 99%

“…Their technique converted textual reviews into sentiment scores, which distinguished the actual hotel attributes that contributed to star ratings. According to their selection criteria, adjectives, adverbs, nouns, and verbs were exploited as candidate sentiment words [ 53 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Ali,

Masood

2024

PLoS ONE

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Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

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“…The results of sentiment analysis were used together with word frequency statistics to dynamically measure the customer satisfaction with hotels. Chen et al [58] built a new sentiment lexicon for hospitality using the PolarityRank algorithm and performed sentiment analysis on a large dataset of online reviews of London hotels. The sentiment analysis result was used as attribute performance in the Kano-IPA model to explain customers' rating behaviors and prioritize attributes for improvement.…”

Section: Text Mining Of Online Hotel Reviewsmentioning

confidence: 99%

Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews

Yuan

You

2022

Sustainability

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The hotel industry has been facing fierce competition in recent years. It is important for hotels to conduct effective strategic planning for competitiveness improvement to achieve sustainable development. Prior studies on hotel strategic planning mainly use questionnaire data or hotel internal data, which have the problems of insufficient data or neglecting customer perspectives. The purpose of this study was to develop an integrated method for customer-oriented strategic planning for hotel competitiveness improvement based on text mining of online reviews. First, text mining of online reviews was conducted to extract customer-concerned service attributes and evaluate customer concern level and the performance of the service attributes through Latent Dirichlet Allocation (LDA) and sentiment analysis. Second, the competitive structures of the hotels were analyzed and the main competitors were identified from the competitive hotels through correspondence analysis. Third, SWOT analysis of the target hotel toward the main competitors was conducted, and the priorities of factors in each SWOT category were determined. An empirical study on a five-star hotel is given to illustrate the feasibility and effectiveness of the proposed method. The results indicate that the proposed method can help managers in strategic planning to obtain more specific strategies for hotel competitiveness improvement.

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Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

Cited by 29 publications

References 99 publications

Evolving Impacts of Attribute Performance on Customer Satisfaction across the Covid-19 Pandemic: An Empirical Case Study in Hospitality

Evolving Impacts of Attribute Performance on Customer Satisfaction across the Covid-19 Pandemic: An Empirical Case Study in Hospitality

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Customer-Oriented Strategic Planning for Hotel Competitiveness Improvement Based on Online Reviews

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