Research on Mining Talent Demand for E-commerce Majors based on LDA Topic Model
DOI:
https://doi.org/10.54097/fcis.v6i2.09Keywords:
LDA Topic Model, Text Mining, Talent Demand Analysis, Text SimilarityAbstract
[Purpose] Based on the text mining method, this study analyzes the job demand for domestic e-commerce industry positions in the Internet-oriented recruitment data, promotes the matching of e-commerce positions and talents, and promotes the good construction of the employment environment in the domestic e-commerce industry.[Method] Use the LDA(Latent dirichlet allocation) topic model for job competency requirements to calculate the similarity of the word segmentation results for professional talent requirements, determine the optimal number of topics, and output the visualization results of the LDA topic model.[Conclusion] In eastern China, there is a high demand for e-commerce jobs, and the majority of these positions only call for 1-3 years of experience. The basic requirement for the majority of jobs is a college degree. The Director of Operations and Director of Network Operations focus on e-commerce operations and management, the Network Promotion Specialist focuses on e-commerce advertising and product promotion, the Data Analyst and Market Analyst focus on data analysis and market research, and the Sales Representative focuses on sales. There is a correlation between the salary of the positions and education, experience and region; the higher the education, the more experience and the higher the level of regional development, the higher the salary level.
Downloads
References
Fernandez R M, Rubineau B. Network recruitment and the glass ceiling: Evidence from two firms[J]. RSF: The Russell Sage Foundation Journal of the Social Sciences, 2019, 5(3): 88-102.
Fellnhofer K. Visualised bibliometric mapping on smart specialisation: A co-citation analysis[J]. International Journal of Knowledge-Based Development, 2018, 9(1): 76-99.
Glänzel W, Debackere K. Various aspects of interdisciplinarity in research and how to quantify and measure those[J]. Scientometrics, 2022, 127(9): 5551-5569.
Wei J, Xu Y. The Application of LDA Model in the Analysis of Job Talent Demand under Big Data Technology[C]//2022 International Conference on Artificial Intelligence in Everything (AIE). IEEE, 2022: 301-305.
Mete M, Yuruk N, Xu X, et al. Knowledge discovery in textual databases: A concept-association mining approach[J]. Data Engineering: Mining, Information and Intelligence, 2010: 225-243.
Xu Haiping et al. Evaluating Online Products Using Text Mining: A Reliable Evidence-Based Approach[J]. International Journal of Semantic Computing, 2022, 16(04).
Cavique Mariana et al. Examining Airbnb guest satisfaction tendencies: a text mining approach[J]. Current Issues in Tourism, 2022, 25(22): 3607-3622.
Spreafico C, Spreafico M. Using text mining to retrieve information about circular economy[J]. Computers in Industry, 2021, 132: 103525.
Orea-Giner A, Fuentes-Moraleda L, Villacé-Molinero T, et al. Does the implementation of robots in hotels influence the overall TripAdvisor rating? A text mining analysis from the industry 5.0 approach[J]. Tourism Management, 2022, 93: 104586.
Manikandan S, Misra S, McCalla S. Drug Repositioning Ketamine as a New Treatment for Bipolar Disorder Using Text Mining[J]. BioChem, 2021, 2(1): 1-7.
Waghmare P. Text mining and word cloud analysis of the articles published in selected library and information science journals over the past decade[J]. International Journal of Information Dissemination & Technology, 2021, 11(3).
Li S, You M, Li D, et al. Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques[J]. Process safety and environmental protection, 2022, 162: 1067-1081.
Xu H, Liu Y, Shu C M, et al. Cause analysis of hot work accidents based on text mining and deep learning[J]. Journal of Loss Prevention in the Process Industries, 2022, 76: 104747.
Rehman A, Aslam N, Abid K, et al. The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining[J]. 2023.
Müller B, Poley C, Pössel J, et al. Livivo–the vertical search engine for life sciences[J]. Datenbank-Spektrum, 2017, 17: 29-34.
Spada I, Chiarello F, Barandoni S, et al. Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy[J]. Technological Forecasting and Social Change, 2022, 182: 121869.
Li J, Yang M, Liu C, et al. Listen to the Companies: Exploring BIM Job Competency Requirements by Text Mining of Recruitment Information in China[J]. Journal of Construction Engineering and Management, 2023, 149(9): 04023076.
Fang F, Zhou Y. A study on recruitment of data analyst based on text mining and visualization technology[C]//Journal of Physics: Conference Series. IOP Publishing, 2021, 1952(4): 042017.
Li Y, Chen X, Mao T, et al. User portrait for archival talents based on recruitment[C]//2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). IEEE, 2021: 116-120.
Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[J]. Journal of machine Learning research, 2003, 3(Jan): 993-1022.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Frontiers in Computing and Intelligent Systems

This work is licensed under a Creative Commons Attribution 4.0 International License.

