The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior
Abstract This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists’ purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emo...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-75794-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850169346783969280 |
|---|---|
| author | Wei Chen |
| author_facet | Wei Chen |
| author_sort | Wei Chen |
| collection | DOAJ |
| description | Abstract This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists’ purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists’ attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists’ purchases. The relationship among tourists, products, and word of mouth (WOM) is represented as a complex network to analyze and predict event occurrence patterns and influencing factors in tourism electronic word-of-mouth (EWOM) data. The construction of the model considers various factors, such as the spread of WOM, the impact of price, etc., to reveal the complex relationships among tourists, WOM, products, etc. Finally, the Recurrent Neural Network (RNN) model is combined with the Backpropagation (BP) model, the time series data is processed with the help of the gated recurrent unit, and the HPN model is trained and evaluated. The Yelp dataset is employed to verify the accuracy and feasibility of the model, which contains the score and review data of many tourist destinations. The results reveal that price, WOM, and destination are one of the main factors influencing tourists’ purchasing behavior, with WOM being the most significant. Positive WOM reviews remarkably increase product sales, while negative WOM has the opposite effect. The minimum expectation for age, occupation, education, personal monthly income, and tourists’ willingness to purchase is 0.00, and the minimum expectation for gender factors is 0.31. The RNN-BP hybrid model has higher accuracy and predictive ability, which is 1.73% and 2.30% more accurate than single models and traditional machine learning predictive models. In short, this study contributes to a better understanding travelers’ needs and preferences to optimize products and services and improve market competitiveness. In addition, the methods and models of this study can also be applied in EWOM data mining in other fields. |
| format | Article |
| id | doaj-art-2c2dfc75f2ff4049bf1594c343b7e919 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2c2dfc75f2ff4049bf1594c343b7e9192025-08-20T02:20:45ZengNature PortfolioScientific Reports2045-23222024-12-0114111610.1038/s41598-024-75794-3The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behaviorWei Chen0School of Hotel and Tourism Management, Shunde Polytechnic UniversityAbstract This study establishes a deep learning model for personalized travel recommendations based on factors that affect tourists’ purchases to provide users with more accurate and personalized travel recommendations. Firstly, Natural Language Processing (NLP) technology is used to process and emotionally analyze tourism review information, dividing it into positive, negative, or neutral to understand tourists’ attitudes towards purchasing products and services. Secondly, a High-Performance Network (HPN) model is constructed based on factors that affect tourists’ purchases. The relationship among tourists, products, and word of mouth (WOM) is represented as a complex network to analyze and predict event occurrence patterns and influencing factors in tourism electronic word-of-mouth (EWOM) data. The construction of the model considers various factors, such as the spread of WOM, the impact of price, etc., to reveal the complex relationships among tourists, WOM, products, etc. Finally, the Recurrent Neural Network (RNN) model is combined with the Backpropagation (BP) model, the time series data is processed with the help of the gated recurrent unit, and the HPN model is trained and evaluated. The Yelp dataset is employed to verify the accuracy and feasibility of the model, which contains the score and review data of many tourist destinations. The results reveal that price, WOM, and destination are one of the main factors influencing tourists’ purchasing behavior, with WOM being the most significant. Positive WOM reviews remarkably increase product sales, while negative WOM has the opposite effect. The minimum expectation for age, occupation, education, personal monthly income, and tourists’ willingness to purchase is 0.00, and the minimum expectation for gender factors is 0.31. The RNN-BP hybrid model has higher accuracy and predictive ability, which is 1.73% and 2.30% more accurate than single models and traditional machine learning predictive models. In short, this study contributes to a better understanding travelers’ needs and preferences to optimize products and services and improve market competitiveness. In addition, the methods and models of this study can also be applied in EWOM data mining in other fields.https://doi.org/10.1038/s41598-024-75794-3Travel recommendationNatural language processing technologyHPN modelRNN-BP hybrid modelData mining |
| spellingShingle | Wei Chen The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior Scientific Reports Travel recommendation Natural language processing technology HPN model RNN-BP hybrid model Data mining |
| title | The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior |
| title_full | The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior |
| title_fullStr | The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior |
| title_full_unstemmed | The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior |
| title_short | The data mining and high-performance network model of tourism electronic word of mouth for analysis of factors influencing tourists’ purchasing behavior |
| title_sort | data mining and high performance network model of tourism electronic word of mouth for analysis of factors influencing tourists purchasing behavior |
| topic | Travel recommendation Natural language processing technology HPN model RNN-BP hybrid model Data mining |
| url | https://doi.org/10.1038/s41598-024-75794-3 |
| work_keys_str_mv | AT weichen thedataminingandhighperformancenetworkmodeloftourismelectronicwordofmouthforanalysisoffactorsinfluencingtouristspurchasingbehavior AT weichen dataminingandhighperformancenetworkmodeloftourismelectronicwordofmouthforanalysisoffactorsinfluencingtouristspurchasingbehavior |