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Clustering Analysis of Hotel Network Reviews Based on Text Mining Method

Author(s)

Yao Wang1, Fuguo Liu1,*, Guodong Li2

Affiliation(s)

1School of Mathematics and Data Science, Changji College, Changji, Xinjiang, China, 2School of Mathematics and Computational Science, Guilin University of Electronic Science and Technology, Guilin, Guangxi, China *Corresponding Author.

Abstract

With the development of information technology, users use online platforms to post real-time online comments to express their preferences and opinions on goods or services. Online review information expresses users' behavioral habits and special preferences. In depth, analysis of hotel online reviews can improve the adaptability of hotel services to user needs. Effective mining of the vast user review data will provide value for the development of the tourism industry. Using text mining methods to process hotel review data, multiple clustering methods were compared and analyzed for positive and negative feature words from the perspective of user experience. It was found that the k-means++ algorithm had a better clustering effect on user network reviews and achieved better clustering and segmentation of user evaluation information. Unsupervised clustering analysis can be used to further classify online comment information into categories based on positive and negative reviews, providing intellectual support for improving the precision and personalized service quality of hotels.

Keywords

Network Comments; Text Mining; Cluster Analysis; Hotel; User Experience

References

[1] Yang L, Maomao C, Qiong S. Sarcasm detection in hotel reviews: a multimodal deep learning approach. Journal of Hospitality and Tourism Technology, 2024, 15(4): 519-533. [2] Krishnan J, Bhattacharjee B, Pratap M, et al. Survival strategies for family-run homestays: Analyzing user reviews through text mining. Data Science and Management, 2024, 7(3): 228-237. [3] Maria P, John G, Bay K O. Consumer-brand heuristics in luxury hotel reviews. Journal of Product & Brand Management, 2024, 33(4): 436-448. [4] Qi Z, Yuling L, Hang D, et al. Public concerns and attitudes towards autism on Chinese social media based on K-means algorithm. Scientific Reports, 2023, 13(1): 15173-15173. [5] Tang Y L, Wang H Z, Wang D S, et al. A novel rough semi-supervised k-means algorithm for text clustering. International Journal of Bio-Inspired Computation, 2023, 21(2): 57-68. [6] Ji R, Yang J, Wu Y, et al. Correction to: Construction and analysis of students’ physical health portrait based on principal component analysis improved Canopy-K-means algorithm. The Journal of Supercomputing, 2024, 80(14): 21561-21562. [7] Shen X G, Jiang Y Z. Optimisation of K-means algorithm based on sample density canopy. International Journal of Ad Hoc and Ubiquitous Computing, 2021, 38(1-2-3): 62-69. [8] Liang Z, Yiqu Z, Yumeng W, et al. Unveiling Patterns and Colors in Architectural Paintings: An Analysis by K-Means++ Clustering and Color Ratio Analysis. Tehnički vjesnik, 2023, 30(6): 1870-1879. [9] Singh A, Koju R. Healthcare Vulnerability Mapping Using K-means ++ Algorithm and Entropy Method: A Case Study of Ratnanagar Municipality. International Journal of Intelligent Systems and Applications (IJISA), 2023, 15(2): 43-54. [10] Li S, Chen J. Virtual human on social media: Text mining and sentiment analysis. Technology in Society, 2024, 78102666-102666. [11] Zolfaghari B, Bibak K, Koshiba T, et al. Statistical Trend Analysis of Physically Unclonable Functions: An Approach via Text Mining. CRC Press: 2021-01-12.