Discovering Opinion Changes in Online Reviews via Learning Fine-grained Sentiments

Abstract

Existing methods focus on destination image construction by textual description or visual content separately. Still, descriptions and images are closely related since they are taken from the aforementioned reviews and represent tourists impression of the metropolis. It'south questionable to study them separately. In this newspaper, nosotros used both images and descriptions from the reviews to construct Eleven'an tourism destination image. More concretely, scene recognition, landmark recognition and food image recognition are utilized to obtain visual prototype. Lexical assay is applied to obtain semantic image. We farther compared the differences between visual image and semantic paradigm then we proposed the fusion image. Finally, the top 300 key words and differences of the photograph contents between the adjacent 2 years are selected to discovering new changes of the destination paradigm. Results prove that the visual image and semantic image are significant different from each other and the new changes of semantic image are closely related to the events or things that happened in that year and changes of visual image are not pregnant.

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Acknowledgements

The piece of work is partially supported by the Philosophy and Social Sciences Projection for Colleges and Universities in Jiangsu Province (nos. 2019SJA0649), National Natural Scientific discipline Foundation of China (nos. 41901174, 61503188).

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Affiliations

1. Faculty of Hospitality and Tourism Direction, Macau Academy of Scientific discipline and Technology, Macau, 999078, Cathay

   Fangqing Sheng & Yang Zhang

2. School of Humanities and Arts, Jiangsu Maritime Institute, Nanjing, 211199, Jiangsu, Communist china

   Fangqing Sheng

3. Nanjing Vocational University of Industry Applied science, Nanjing, 210023, Jiangsu, China

   Cheng Shi

4. Institute of Quantitative and Technical Economic science, Chinese Academy of Social Scientific discipline, Beijing, 100732, Mainland china

   Cheng Shi

5. Higher of Economics and Management, Nanjing Forestry Academy, Nanjing, 210037, Jiangsu, China

   Mengyuan Qiu

6. Schoolhouse of Information science and Technology, Nanjing Normal University, Nanjing, 210023, Jiangsu, Mainland china

   Shuaizhen Yao

Corresponding writer

Correspondence to Yang Zhang.

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Cite this article

Sheng, F., Zhang, Y., Shi, C. et al. Xi'an tourism destination image analysis via deep learning. J Ambient Intell Man Comput (2020). https://doi.org/10.1007/s12652-020-02344-westward

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• Received: 15 January 2020

• Accustomed: 11 July 2020

• Published: 18 July 2020

• DOI : https://doi.org/10.1007/s12652-020-02344-w

Keywords

• Deep learning
• Fine-grained paradigm recognition
• Scene recognition
• Landmark recognition
• Destination paradigm

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