Abstract
We generally use the extraction of keywords to study the factors affecting the growth of excellent teachers. However, an automatic research algorithm is needed to let us understand more clearly when there are many keywords and more complex which aspect has greater influence on teachers. Therefore, this paper proposes an unsupervised learning clustering method based on keyword extraction. First, segments the text and use the Word2Vec tool to train the word vector of the segmentation results; secondly, uses the TF-IDF algorithm to extract the keywords of the text, and extracts five from each document; finally, uses the extracted keywords as the clustering sample, through the Kmeans algorithm obtains the clustering result and manually marks the clustering results to evaluate the clustering effect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhao, X.-P., Huang, Z.-Y.: A short text clustering algorithm combining TF-IDF method and word vector. Electron. Design Eng. 28(443, 21), 11–15 (2020)
Mikolov, T., Chen, K., Corrado, G., et al. Efficient estimation of word representations in vector space. Comput. Sci. (2013)
Bengio, Y., Ducharme, R., Vincent, P., et al.: A neural probabilistic language model. J. Mach. Learn. Res. (2003)
Li, X.-R., **a, Y.: Interest point recommendation algorithm based on similarity fusion and dynamic prediction Comput. Eng. Appl. 54(905, 10), 110–114+217 (2018)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning, pp. 1188–1196 (2014)
Yuan, H.-C.: Research on the spectral clustering method of the complex network of the adjacent surface of the three-dimensional model. Shandong Normal University (2014)
Lin, J.-H., Zhou, Y.-M.: Analysis of the evolution of news comment topics combined with word vectors and clustering algorithms. Comput. Eng. Sci. 38(11), 2368–2374 (2016)
Li, X.-M.: Research on internet personal consumption loan pricing based on clustering algorithm. Shanghai University of Finance and Economics (2014)
Mao, Y.-X., Qiu, Z.-X.: Research on information technology document clustering based on Word2Vec model and K-means algorithm. China Inf. Technol. Educ. (008), 99–101 (2020)
Acknowledgement
The work was supported by the Doctoral Foundation of Tian** Normal University (52XB2004), the Natural Science Foundation of China (62001328) and TJNU “Artificial Intelligence + Education” United Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, X., Ding, X., Wang, W., Wang, G., Kang, Y., Wang, S. (2022). Keywords Clustering for the Interview Texts Based on Kmeans Algorithm. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_75
Download citation
DOI: https://doi.org/10.1007/978-981-16-9423-3_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9422-6
Online ISBN: 978-981-16-9423-3
eBook Packages: Computer ScienceComputer Science (R0)