Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism
Abstract This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. I...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85139-3 |
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| author | Tingting Mou Hongbo Wang |
| author_facet | Tingting Mou Hongbo Wang |
| author_sort | Tingting Mou |
| collection | DOAJ |
| description | Abstract This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA). The Bidirectional Recurrent Neural Network (BiGRU) can effectively capture the temporal relationship and semantic dependence in the text through its powerful sequence modeling ability, thus achieving a more accurate classification of emotional tendencies. In order to verify the performance of the proposed ATT-LDA- Bigelow model, online comments about tourist attractions are collected from Ctrip.com, and users’ emotional tendencies towards different scenic spots are analyzed. The results show that this model has the best emotion classification effect in online comments of scenic spots, with the accuracy and F1 value reaching 93.85% and 93.68% respectively, which is superior to other emotion classification models. The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. This achievement is expected to bring new enlightenment and breakthrough to the research and practice in related fields. |
| format | Article |
| id | doaj-art-6bf8ba08ebef42edae8c5627ae4cf9f6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6bf8ba08ebef42edae8c5627ae4cf9f62025-01-12T12:16:12ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-85139-3Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanismTingting Mou0Hongbo Wang1School of Hospitality Management, China University of Labor RelationsSchool of New Media, Peking UniversityAbstract This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA). The Bidirectional Recurrent Neural Network (BiGRU) can effectively capture the temporal relationship and semantic dependence in the text through its powerful sequence modeling ability, thus achieving a more accurate classification of emotional tendencies. In order to verify the performance of the proposed ATT-LDA- Bigelow model, online comments about tourist attractions are collected from Ctrip.com, and users’ emotional tendencies towards different scenic spots are analyzed. The results show that this model has the best emotion classification effect in online comments of scenic spots, with the accuracy and F1 value reaching 93.85% and 93.68% respectively, which is superior to other emotion classification models. The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. This achievement is expected to bring new enlightenment and breakthrough to the research and practice in related fields.https://doi.org/10.1038/s41598-025-85139-3Artificial intelligenceText miningAttention mechanismTourist attractionsOnline comment |
| spellingShingle | Tingting Mou Hongbo Wang Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism Scientific Reports Artificial intelligence Text mining Attention mechanism Tourist attractions Online comment |
| title | Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism |
| title_full | Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism |
| title_fullStr | Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism |
| title_full_unstemmed | Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism |
| title_short | Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism |
| title_sort | online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism |
| topic | Artificial intelligence Text mining Attention mechanism Tourist attractions Online comment |
| url | https://doi.org/10.1038/s41598-025-85139-3 |
| work_keys_str_mv | AT tingtingmou onlinecommentsoftouristattractionscombiningartificialintelligencetextminingmodelandattentionmechanism AT hongbowang onlinecommentsoftouristattractionscombiningartificialintelligencetextminingmodelandattentionmechanism |