Author(s)

Yuan Long ¹, Ning Wang ¹, Xingmei Xu ²

Affiliation(s)

¹ College of Data Science, Guangzhou Huashang College, Guangzhou, 511300, China
² College of Information Technology, Jilin Agricultural University, Changchun, 130118, China

Corresponding Author

ABSTRACT

In the interpretation of the concept of smart scenic spot, this article takes Changchun Jingyuetan scenic spot as an example, and puts forward the accurate suggestion of obtaining smart scenic spot from tourists by using social network (SNS) data. This paper uses a series of data mining technology, through obtaining China more influential tourist communication platform, Meituan, ctrip and weibo comment data, collect Jingyuetan scenic spot tourists accurate ridicule, characteristic word frequency analysis and clustering grid analysis, and summarizes the wisdom of Jingyuetan scenic spot environment, scenic area service, scenic area traffic three aspects put forward the scientific and effective rectification reference Suggestions and empirical research. This research provides a new idea and an important reference for the precise improvement research of smart scenic spots around the world, and is of great significance for the construction of smart scenic spots.

KEYWORDS

CITE THIS PAPER

Yuan Long, Ning Wang, Xingmei Xu, Research on the Decision Making of Jingyuetan Scenic Spot Based on Social Network Data Processing. Tourism Management and Technology Economy (2023) Vol. 6: 30-42. DOI: http://dx.doi.org/10.23977/tmte.2023.060304.

REFERENCES

[1] Chen Jianbin, Zheng Li, Zhang Lingyun. Research on IT Ability Model and Its Core Composition in Smart Scenic Spot. Tourism Science, 2014, 28 (1): 14-21.
[2] Zhang Lingyun, Li Liumin. The basic concept and Theoretical system of Smart tourism. Tourism Journal, 2012, 27 (5): 66-73.
[3] Dang Anrong, Zhang Danming, Ma Qiwei, et al. Discussion on Smart Scenic Spot Management and Service in the Era of Big Data. Western Habitat Environment Journal, 2016, 31 (4): 8-13.
[4] Anchal Gupta, Satish Mahadevan Srinivasan. Constructing a Heterogeneous Training Dataset for Emotion Classification. Procedia Computer Science, 2020, 168.
[5] Dey A, Jenamani M, Thakkar J J. Lexical TF-IDF: An n-gram feature space for cross-domain classification of sentiment reviews. International Conference on Pattern Recognition and Machine Intelligence. Springer, Cham, 2017: 380-386.
[6] Rayner M, Tsourakis N, Gerlach J. Lightweight spoken utterance classification with CFG, tf-idf and dynamic programming. International Conference on Statistical Language and Speech Processing. Springer, Cham, 2017: 143-154.
[7] Li Y, Shen B. Research on sentiment analysis of microblogging based on lsa and tf-idf.3rd IEEE International Conference on Computer and Communications, Chengdu, China. 2017: 2584-2588.
[8] Irfan M, Zulfikar W B. Implementation of Fuzzy C-Means algorithm and TF-IDF on English journal summary. 2nd International Conference on Informatics and Computing, Papua, Indonesia. 2017: 1-5.
[9] Gernein. Hotel Review Emotion Analysis based on Dictionary and Machine Learning. Jiangsu University of Science and Technology, 2019.
[10] Wu Sihui, Chen Shiping. Spam SMS identification of the Self-Attention-Based Bi-LSTM combined with TFIDF. Computer system applications, 2020, 29 (09): 171-177.
[11] Qu Dandan, Yang Tao, Hu Kongfa. Application of NLP in Automatic Collection of Symptoms Information. Software Guide, 2021, 20 (02): 44-48.
[12] Branco Ponomariov. Government-sponsored University-industry Collaboration and the Production of Nanotechnology Patents in US Universities. Journal of Technology Transfer, 2013, 38 (6): 749-767.

Sponsors, Associates, and Links

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Journal of Computational and Financial Econometrics
  
  
• Financial Engineering and Risk Management
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics