Academic Journal of Computing & Information Science, 2021, 4(4); doi: 10.25236/AJCIS.2021.040410.
Cao Jingjing, Wang Ye, Wang Ying, Zhang Xiaoxia, Zhang Xue
Exchange, Development & Service Center for Science &Technology Talents, The Ministry of Science &Technology (MoST), Beijing, China
In order to solve the problem of low clustering contour coefficient caused by inaccurate keyword extraction of talent data information, a talent data analysis method based on text mining is proposed. Through the preprocessing of word segmentation and stop words, the text set of talent data is established, and the keyword graph is constructed by text mining, and the information keywords are obtained according to the weight iteration results. The keywords in this paper are transformed into the form of multi-dimensional vector, and the similarity is calculated to get the results of text analysis. The experimental results show that the contour coefficient of the proposed method is 0.736, which is 0.267 and 0.221 higher than that of the K-means and single pass methods, respectively. The design method of this paper has a reasonable clustering performance, which is suitable for talent big data analysis.
text mining; Talent data analysis; Talent demand; Text representation; Text segmentation; Clustering effect
Cao Jingjing, Wang Ye, Wang Ying, Zhang Xiaoxia, Zhang Xue. Research on talent data analysis method based on Text Mining. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 4: 56-59. https://doi.org/10.25236/AJCIS.2021.040410.
 LI Xinqin, MA Xiaoning, WANG Zhe, et al.Performance analysis of railway safety supervision personnel based on Text Mining Technology[J].Railway Computer Application, 2019,28(10):30-34.
 ZENG Li, CAI Yuxia, ZHANG Jiantao, et al. On the cultivation of information management professionals based on the text mining of employment market demand[J]. Modern computer,2019(21): 59-64.
 FU Xiao. Analysis of talent training cycle based on multi-source text similarity [J]. Electronic technique, 2020, 49(08): 114-115.