How do Mainland Chinese tourists perceive Hong Kong in turbulence? A deep learning approach to sentiment analytics

Hao, Jie; Wang, Rui; Law, Rob; Yu, Yan

doi:10.1002/jtr.2419

Cited by 12 publications

(5 citation statements)

References 54 publications

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“…Many studies in the tourism and hospitality sectors have focused on travel sentiment analysis. Tese studies, however, focused on assessing the opinions posted by tourists on multiple platforms using sentiment analysis and opinion-mining techniques [27][28][29]. Because of this, despite their semantic similarity, the travel sentiment examined in this paper and the sentiment conveyed in reviews are not the same.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Lee

Kong

2023

Journal of Mathematics

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The tourism industry has become a major contributor to the economic growth. However, because of the outbreak of the coronavirus disease (COVID-19) pandemic, the year 2020 became an extremely difficult year for the global tourism industry. Since the development of the tourism industry depends largely on changes in travel sentiment, it is important to analyze these changes in light of the pandemic. To determine the development trends of travel sentiment, a hybrid grey prediction model was used to predict travel sentiment globally and in the top 10 destination countries considering the shock effect of COVID-19 and vaccination. The results showed that the grey prediction models integrated with residual modification model contributed to improving the prediction accuracy. In addition, COVID-19 and vaccination were found to have opposite effects on travel sentiment. Based on the predictions, governments should strengthen pandemic prevention and control and administer vaccines to restore travel sentiment and promote tourism recovery.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Lee

Kong

2023

Journal of Mathematics

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“…Also, the text review analysis helps calculate the factors that affect user satisfaction (Cui et al , 2023; Gour et al , 2021). Hao et al (2021) propose a CNN-based approach to analyze reviews, that identify the sentiment and tone of the review and highlight positive and negative feature lists of words. In addition, textual information analysis can be used to assess the usefulness of reviews (Lee et al , 2021) and detect and create fake reviews (Lee et al , 2022).…”

Section: Related Workmentioning

confidence: 99%

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Glukhov,

Derevitskii,

Severiukhina

et al. 2023

JHTT

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Purpose Using the data set about the restaurants from different countries and their customer's feedback, the purpose of this paper is to address the following issues: in the restaurant industry, how have user behavior and preferences changed during the COVID-19 restrictions period, how did these changes influence the performance of recommendation algorithms and which methods can be proposed to improve the quality of restaurant recommendations in a lockdown scenario. Design/methodology/approach To assess changes in user behavior and preferences, quantitative and qualitative data analysis was performed to assess the changes in user behavior and preferences. The authors compared the situation before and during the COVID-19 restrictions period. To evaluate the performance of restaurant recommendation systems in a non-stationary setting, the authors tested state-of-the-art collaborative filtering algorithms. This study proposes and investigates a filtering-based approach to improve the quality of recommendation algorithms for a lockdown scenario. Findings This study revealed that during the COVID-19 restrictions period, the average rating values and the number of reviews have changed. The experimental study confirmed that: the performance of all state-of-the-art recommender systems for the restaurant industry has significantly degraded during the COVID-19 restrictions period; and the accuracy and the stability of restaurant recommendations in non-stationary settings may be improved using the sliding window and post-filtering methods. Practical implications The authors propose two novel methods: the sliding window and closed restaurants post-filtering method based on the CatBoost classification model. These methods can be applied to classical collaborative recommender algorithms and increase the value of metrics under non-stationary conditions. These methods can be helpful for developers of recommender systems and massive aggregators of restaurants and hotels. Thus, it benefits both the app end-user and business owners because users honestly rate restaurants when they receive good recommendations and do not downgrade because of external factors. Originality/value To the best of the authors’ knowledge, this paper provides the first extensive and multifaceted experimental study of the impact of COVID-19 restrictions on the effectiveness of restaurant recommendation systems in different countries. Two novel methods to tackle restaurant recommendations' performance degradation are proposed and validated.

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“…Because of the large amount of data generated from social media (blogs, micro-blogs, reviews, etc. ), it is necessary to summarize this data to obtain useful information for tourism managers and travelers (Hao et al, 2020a(Hao et al, , 2020b and SA provides people's opinion or feedback about a product or destination. Alaei et al (2019) examine different SA approaches to identify the performance of each one using different data sets.…”

Section: Sentiment Analysis and Tourismmentioning

confidence: 99%

“…are, therefore, widely used to mine public opinion about services or products. There are many supervised and unsupervised machine learning-based techniques that have been developed to identify text polarity and, in recent years, more accurate deep learning methods have been developed and used in text classification (Hao et al, 2020a(Hao et al, , 2020bKirilenko et al, 2018;Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Discovering a tourism destination with social media data: BERT-based sentiment analysis

Viñán-Ludeña

Campos

2022

JHTT

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Purpose The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users. Design/methodology/approach The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models. Findings The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated. Research limitations/implications The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place. Practical implications The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions. Originality/value This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

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How do Mainland Chinese tourists perceive Hong Kong in turbulence? A deep learning approach to sentiment analytics

Cited by 12 publications

References 54 publications

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Forecasting Travel Sentiment under the Shock Effects of COVID-19 and Vaccination Using Grey Prediction Models

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

Discovering a tourism destination with social media data: BERT-based sentiment analysis

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