ABSTRACT
Recruitment industry is better and bigger than ever. There is no denying that technology plays a major role in helping recruiters evolve and adopt with the pace of recruitment on a global scale. With the increasing population, the demand for manpower has been relative to the growth and challenging needs of recruiters; be it online or traditional way of outsourcing. In this study, we propose a combination of angle or similarity and term frequency–inverse document frequency to easily classify prospective job applicants. The results show that the two models are relative to each other, value-wise and harmonic means. Their values are synchronized to a certain extent based on our query. This is helpful because recruiters may save a lot of time in classifying prospective applicants. It can also be concluded that harmonic similarity is viable in combining the two models. As a future work, it is possible to develop a full featured application to be deployed in a production setting.
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- J. Hartmann, J. Huppertz, C. Schamp and M. Heitmann, "Comparing automated text classification methods," International Journal of Research in Marketing , 24 October 2018.Google Scholar
- "7 Recruitment trends for 2020," 12 September 2019. [Online]. Available: https://www.pageuppeople.com/resource/7-recruitment-trends-for-2020/.Google Scholar
- Z. Wu, H. Zhu, G. Li, Z. Cui, H. Huang, J. Li, E. Chen and G. Xu, "An efficient Wikipedia semantic matching approach to text document classification," Information Sciences (2017), 3 February 2017.Google Scholar
- J. Z. Bernal Jime ́nez Gutie ́rrez, D. Zhang, P. Zhang and Y. Su, "Document Classification for COVID-19 Literature," 2020.Google Scholar
- S. Li and T. Forss, "Text Classification Models for Web Content Filtering and Online Safety," 2015 IEEE 15th International Conference on Data Mining Workshops, 2015.Google Scholar
- T. Thongtan and T. Phienthrakul, "Sentiment Classification using Document Embeddings trained with Cosine Similarity," in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Florence, 2019.Google Scholar
- B. Li and L. Han, "Distance Weighted Cosine Similarity Measure for Text Classification," International Conference on Intelligent Data Engineering and Automated Learning, pp. 611-618, 2013.Google Scholar
- A. Singhal, "Modern Information Retrieval: A Brief Overview," in Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 2001.Google Scholar
- H. Yu, J. Han and K. C.-C. Chang, "PEBL: Web Page Classification without Negative Examples," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 16, 2004.Google Scholar
- S. Fong, Y. Zhuang and J. He, "Not Every Friend on a Social Network Can be Trusted: Classifying Imposters Using Decision Trees," 2012.Google Scholar
- X. Jiang, M. Ringwald, J. A. Blake, C. Arighi, G. Zhang and H. Shatkay, "An effective biomedical document classification scheme in support of biocuration: addressing class imbalance," Database, 18 March 2019.Google Scholar
- A. C. Alberto Blanco, A. Pérez and A. D. d. Ilarraza, "Multi-label clinical document classification: Impact of label-density," xpert Systems With Applications, 22 July 2019.Google Scholar
- Y. Xiao and K. Cho, "Efficient Character-level Document Classification by Combining Convolution and Recurrent Layer," 2016.Google Scholar
- S.-H. Cho and H.-B. Kang, "Statistical text analysis and sentiment classification in social media," 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012.Google Scholar
- I. Masaru, F. Toru, M. Junichiro, M. Yutaka and S. Ichiro, "Extractive Summarization Using Multi-Task Learning with Document Classification," Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, p. 2101–2110, 11 September 2017.Google Scholar
- D. Isa, L. H. Lee, V. Kallimani and R. RajKumar, "Text Document Preprocessing with the Bayes Formula for Classification Using the Support Vector Machine," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 20, pp. 1264-1272, September 2008.Google ScholarDigital Library
- C. W. Kim, "Document Classification Part 1: Intuition & How Do We Work With Documents?," 18 February 2018. [Online]. Available: https://medium.com/machine-learning-intuition/document-classification-part-1-intuition-54baf716757f.Google Scholar
- S. V. Donge and A. Enright, "Metric Distances Derived From Cosine Similarity And Pearson And Spearman Correlations," 2012.Google Scholar
- H. Attri, "Feature Extraction using TF-IDF algorithm," 27 October 2019. [Online]. Available: https://medium.com/@hritikattri10/feature-extraction-using-tf-idf-algorithm-44eedb37305e.Google Scholar
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