Sentiment of Chinese Tourists towards Malaysia Cultural Heritage Based on Online Travel Reviews

Cao, Zheng; Xu, Heng; Teo, Brian Sheng-Xian

doi:10.3390/su15043478

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Figure 8 depicts the performance of the proposed MQ-LSTM-based RS with existing systems developed by [23,24,26]. From the analysis, it is clear that by obtaining a high RR of 97.22%, the proposed model exhibited superior performance.…”

Section: Performance Analysis Of the Recommendation Systemmentioning

confidence: 99%

“…However, with fuzzy logic, inappropriate decision-making rules would deteriorate the assessment results. [26] recommended a model for the sentiment analysis of Chinese tourists toward Malaysia's cultural heritage grounded on online travel reviews. The sentiment analysis leveraged vocabulary filtering, semantic clustering technology, and BERT, along with co-occurrence analysis.…”

Section: Related Workmentioning

confidence: 99%

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Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Jeribi,

Ahamed,

Perumal

et al. 2023

Sustainability

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Cultural tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for peak visitor time in Riyadh, a city in Saudi Arabia. The main objective of the framework is to improve the cultural tourism of Riyadh by considering various factors to help in improving CT based on recommendation system (RS). Primarily, the map data and cultural event dataset were processed for location, such as grouping with Kriging interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes were processed with word embedding. Meanwhile, the social network sites (SNS) data like reviews and news were extracted with an external application programming interface (API). The review data were processed with keyword extraction and word embedding, whereas the news data were processed with score value estimation. Lastly, the data were fused, corresponding to a historical site, and given to the Multi-Quadratic-Long Short-Term Memory (MQ-LSTM) recommendation system (RS); also, the recommended result with the map was stored in a database. Lastly, the database security was maintained with locality sensitive hashing (LSH). From the experimental evaluation with multiple databases including the Riyadh Restaurants 20K dataset, the proposed recommendation model achieved a recommendation rate (RR) of 97.22%, precision of 97.7%, recall of 98.27%, and mean absolute error (MAE) of 0.0521. This result states that the proposed RS provides higher RR and reduced error compared to existing related RSs. Thus, by attaining higher performance values, the proposed model is experimentally verified.

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Section: Performance Analysis Of the Recommendation Systemmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Jeribi,

Ahamed,

Perumal

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Duan et al [47] used Bert instead of Word2vec extracted word vector as the model input, improving text classification's effect. Cao et al used the Bert model to analyze the sentiment of online reviews of nine attractions in Melaka, and the results showed that the BERT-based deep learning approach can obtain improved sentiment predictive performance [7]. Deep learning-based models have significant advantages over the first two approaches in that they can process complex, large-scale information more efficiently.…”

Section: Research On Text Sentiment Analysis Techniquesmentioning

confidence: 99%

“…Deep learning, as a branch in the field of machine learning, is capable of acquiring high-level features from data, digging deeper into the perceived elements of a destination's image, and focusing on the deeper information behind the emotional changes of travelers when experiencing a destination. Currently, scholars have paid attention to the application of deep learning models such as LSTM [6], Bert [7], and CNN [8] on image perception of tourist destinations and confirmed the advantages of deep learning models in processing tourist review texts. Tourism review texts may contain content unrelated to the destination image, which may affect our understanding of the destination image, which will be a difficulty and a challenge when we study the destination image.…”

Section: Introductionmentioning

confidence: 99%

Research on Image Perception of Tourist Destinations Based on the BERT-BiLSTM-CNN-Attention Model

Wen,

2024

Sustainability

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Image perception of tourism destinations plays an important role in destination marketing and management. Considering that long-distance feature information of travel review text is difficult to capture and local key information is ignored, BiLSTM and CNN are improved to propose a travel text classification method based on BERT-BiLSTM-CNN-Attention hybrid neural network model. Taking Sanya City as the research object, we adopt the emotion classification and content analysis methods and construct the tourism destination image perception analysis framework based on the “cognitive-emotional” three-dimensional model, providing a research perspective for the sustainable development of tourism in Sanya City. The results show that the accuracy of the proposed model reaches 93.18%, which is better than other models. Tourists’ perception of destination image includes four aspects: tourism resources, tourism environment perception, tourism infrastructure and supporting services, as well as tourism activities. Positive emotions dominate emotional image, and negative emotions are mainly focused on tourism infrastructure and supporting services. On the overall image perception, tourists have a high evaluation of the tourism image of Sanya City. This research has some implications for tourism destinations, such as improving their management programs, enhancing their marketing strategies, and achieving long-term sustainable development of their destinations.

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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Sentiment of Chinese Tourists towards Malaysia Cultural Heritage Based on Online Travel Reviews

Cited by 7 publications

References 37 publications

Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Research on Image Perception of Tourist Destinations Based on the BERT-BiLSTM-CNN-Attention Model

Machine learning applied to tourism: A systematic review

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