Abstract
The scenic comment information from visitors often hidden the different aspects of the recommendations and expectations of the attractions, the extraction of these key information will help the spot managers find their own shortcomings and improve themselves. In this paper, we improved the author topic model and proposed a model of clustering the negative comments of the scenic spots. There are two improvements from our proposed model. Firstly, we added the importance of the comment category to the text clustering. Secondly, in order to prevent the stop words accumulating in the sampling process, we introduced the balance weight to the proposed model. Experiments showed that the model could not only effectively cluster these data, but also could extract the rich information related to different comment categories from the clustering results, which could help the managers of the scenic spots to better manage the attractions and attract tourists.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61320106006, No. 61532006, No. 61502042).
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Lin, Z., Du, J., Li, Y., Ye, L., Luo, A. (2018). Scenic Negative Comment Clustering Based on Balance Weighted Comment Topic Model. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-6496-8_28
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DOI: https://doi.org/10.1007/978-981-10-6496-8_28
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