Motivation behind review provision in online travel communities: do hometowner contributions matter?

Liang, Sai; Ye, Qiang; Zhang, Xiaoxia; Law, Rob; Gong, Caiyan

doi:10.1108/ijchm-08-2021-0980

Cited by 6 publications

(2 citation statements)

References 47 publications

(84 reference statements)

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“…Pooling Layer: After feature extraction is completed in the convolutional layer, the resulting feature map is subjected to processing in the pooling layer. The main purpose of the pooling layer is to reduce the size of the feature map, thereby compressing the data and improving the network's ability to generalize [25][26][27]. This layer is also known as the downsampling layer.…”

Section: A Cnnsmentioning

confidence: 99%

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

Meng

2024

IEEE Access

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The relentless progression of advanced technologies has driven the seamless integration of Internet of Things (IoT) services into the fundamental framework of contemporary tourism enterprises. In the quest for valuable insights from the vast reservoir of tourism data, this study employs a Convolutional Neural Network (CNN) as its primary instrument, culminating in the development of a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model designed for the classification of textual data in the smart tourism domain. The primary function of this model is to conduct sentiment analysis on smart tourism data, specifically by categorizing review data into either positive or negative sentiments. The incorporation of review ratings further aids in the accurate labeling of data according to their respective sentiment categories, thus streamlining the process of effective data annotation. The empirical findings reveal distinctive performance trajectories for the three models concerning positive comments across various evaluation metrics. Remarkably, in terms of precision, the CNN-BiLSTM model leads with an impressive 87.9%, closely followed by the BiLSTM model at 87.4%, while the Text Convolutional Neural Network (TextCNN) model trails slightly at 85.3%. Similarly, the recall highlights the CNN-BiLSTM model's excellence, achieving an impressive 88.34%, compared to 78.5% for BiLSTM and 77.6% for TextCNN models. In the realm of accuracy, the CNN-BiLSTM model maintains its dominance at 85.1%, while the BiLSTM and TextCNN models achieve 82.7% and 81.4%, respectively. Notably, the CNN-BiLSTM model outperforms the trio in terms of the F1 value, securing a robust 85.41%. In summary, the CNN-BiLSTM model consistently demonstrates excellent performance across a range of metrics, including precision, recall, accuracy, and F1 value, signifying its supremacy in this classification task. This study presents a systematic solution for enhancing smart tourism services, thereby providing a strong foundation for the growth and advancement of tourism enterprises.

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Section: A Cnnsmentioning

confidence: 99%

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

Meng

2024

IEEE Access

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“…Wang et al (2018) demonstrated the effect of company image on the formation of favorable word-of-mouth associations. Liang et al (2022) reported that various online travel companies use customer reviews to persuade new customers to stay at hotels that are advertised on their mobile applications. Many hotels also offer online incentives to customers who submit reviews.…”

Section: Word-of-mouthmentioning

confidence: 99%

Why Should Hotels Become More Environmentally Friendly? Does Gender Matter in Customer Feedback?

Ru-zhe,

Aujirapongpan,

Phetvaroon

et al. 2023

ABAC J

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A year after the Coronavirus 2019 (COVID-19) pandemic, the hotel industry is showing signs of recovery. Environmental issues have also increased the pressures on this sector. As such, this study examines the effect of eco-friendly hotel practices on customer feedback in the post-COVID-19 era. The relationships between green practices, customer satisfaction, and loyalty to green hotels, and the effect of customer feedback on sustainability through intentions to revisit and word-of-mouth are also examined. In addition, the role of customer satisfaction and loyalty to green hotels as mediators is considered while the role of gender in these relationships is explored. PLS-SEM and PLS-MGA were used to create a cross-sectional descriptive and analytical design. Results demonstrate a significant association between the implementation of ecologically friendly practices and consumer satisfaction and loyalty to green hotels. Word-of-mouth and COVID-19 concerns have a substantial effect on intentions to revisit. The main findings are that in terms of associations between design and landscape and revisit intentions, women are more loyal to green hotels than men. In conclusion, the positive association between green practices and various guest-related outcomes implies that hotels aiming for sustainability must prioritize effective communication, strategic branding, employee engagement, and ongoing improvement. Females’ loyalty to green hotels and intentions to revisit have been found to be strongly correlated, providing hotels with the chance to establish a distinctive brand and win over a devoted clientele. Additionally, environmental policy is confirmed to enhance sustainable practices in the hotel sector.

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Substitution or complementary effects between hosts and neighbors’ information disclosure: Evidence from Airbnb

Zheng

Zhang

et al. 2023

Journal of Business Research

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Motivation behind review provision in online travel communities: do hometowner contributions matter?

Cited by 6 publications

References 47 publications

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things

Why Should Hotels Become More Environmentally Friendly? Does Gender Matter in Customer Feedback?

Substitution or complementary effects between hosts and neighbors’ information disclosure: Evidence from Airbnb

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