A Novel Approach for Job Mining and Trend Summarization on Social Media Posts

Ramdas Gawande, Nilesh J. Uke

Abstract


In the job classification field, precise classification of jobs to profession categories is important for harmonizing job seekers with appropriate jobs. An example of such a job title classification system is an automatic text job post classification system that utilizes machine learning. Machine learning based job type classification techniques for text and related entities have been well researched in academia and have also been successfully applied in many industrial settings. Digital recruitment is a popular online method that has been widely used for attracting individuals who are seeking for career opportunities. In recent years digital recruitment is transforming from passive websites such as Monster and Career Builder.

In this paper we present a novel approach, a machine learning- based semi-supervised job title classification system. Our method leverages a varied collection of classification and techniques to tackle the challenges of designing a scalable classification system for a large taxonomy of job categories. It encompasses these techniques in cascade classification architecture. We first present the architecture of our system, which consists of a two-stage Capture with filtration and fine level classification algorithm. The paper concludes by presenting experimental results on real world live data.


Full Text:

PDF

References


PuneetGarg, Rinkle Rani, SumitMiglani, ”Mining Professional’s Data from LinkedIn”, Fifth International Conference on Advances in Com- puting and Communications, 2015.

Rathore, Muhammad MazharUllah, Anand Paul, Awais Ahmad, Bo-Wei Chen, Bormin Huang, and Wen Ji., ”Real-Time Big Data Analytical Architecture for Remote Sensing Application”, IEEE’s Journal of Selected Topics in Applied EarthObservations and Remote Sensing, 2015.

Diaby, Mamadou, Emmanuel Viennet, and Tristan Launay, ”Toward the Next Generation of Recruitment Tools : An Online Social Network- based Job Recommender System”, IEEE/ACM International onference on Advances in Social Networks Analysis and Mining - ASONAM 13, 2013..

Javed, Faizan, Qinlong Luo, Matt McNair, Ferosh Jacob, Meng Zhao, and Tae Seung Kang, ”Carotene: A Job Title Classification System for the Online Recruitment Domain”, IEEE’s First International Conference on Big DataComputing Service and Applications, 2015.

Namrata Gawande, Ramdas Gawande, ”Processing of Real Time Big Data for Machine Learning”, International Journal of Advanced Research in Computer and Communication Engineering, 2016.

Ahmed AbdeenHamed, Xindong Wu, James R Fingar, ”A Twitter-based Smoking Cessation Recruitment System”, IEEE’s International Confer- ence on Advances in Social Networks Analysis and Mining, 2013.

MamadouDiaby , Emmanuel Viennet, ”Taxonomy-based Job Recom- mender Systems On Facebook and LinkedIn Profiles”, IEEE’s Eighth International Conference on Research Challenges in Information Science (RCIS), 2014.

Emmanuel Malherbe, MamadouDiaby, Mario Cataldi, Emmanuel Vien- net, Marie- AudeAufaure, ”Field Selection for Job Categorization and Recommendation to Social Network Users”, IEEE’s International Con- ference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014.


Refbacks

  • There are currently no refbacks.


 

Copyright © IJETT, International Journal on Emerging Trends in Technology