Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG
This project presents a novel approach to tackling the lack of consistency within the job market in order to enhance career transition strategies within the workplace. There are 2 primary objectives within this project, the first objective involves the classification of online job descriptions in...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176536 |
| _version_ | 1811677689021464576 |
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| author | Ong, Yuan Sheng |
| author2 | S Supraja |
| author_facet | S Supraja Ong, Yuan Sheng |
| author_sort | Ong, Yuan Sheng |
| collection | NTU |
| description | This project presents a novel approach to tackling the lack of consistency within the job market
in order to enhance career transition strategies within the workplace. There are 2 primary
objectives within this project, the first objective involves the classification of online job
descriptions into a coherent framework, delineating the requisite skills for each role. Utilizing
datasets provided by ARISE SG, such as 'Taxonomy.xlsx' and 'sampled1000.xlsx', this study
implements natural language processing (NLP) techniques to create a correlation between job
titles and their associated skill sets, despite variances across different corporate interpretations.
The second objective seeks to quantitatively assess the difficulty of transitioning between job
roles using the Skills Framework Database.
Various methodologies were employed within this project such as data preprocessing to
eliminate inconsistencies, application of a BERT transformer model for data labeling, and
development of a machine learning model for skill association and predictive analytics.
Through this project, we aim to not only provide more insights into the current job market but
also develop a strategic tool for career development in the rapidly evolving job market. |
| first_indexed | 2024-10-01T02:41:21Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176536 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:41:21Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1765362024-05-17T15:45:59Z Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG Ong, Yuan Sheng S Supraja School of Electrical and Electronic Engineering supraja.s@ntu.edu.sg Engineering Machine learning NLP This project presents a novel approach to tackling the lack of consistency within the job market in order to enhance career transition strategies within the workplace. There are 2 primary objectives within this project, the first objective involves the classification of online job descriptions into a coherent framework, delineating the requisite skills for each role. Utilizing datasets provided by ARISE SG, such as 'Taxonomy.xlsx' and 'sampled1000.xlsx', this study implements natural language processing (NLP) techniques to create a correlation between job titles and their associated skill sets, despite variances across different corporate interpretations. The second objective seeks to quantitatively assess the difficulty of transitioning between job roles using the Skills Framework Database. Various methodologies were employed within this project such as data preprocessing to eliminate inconsistencies, application of a BERT transformer model for data labeling, and development of a machine learning model for skill association and predictive analytics. Through this project, we aim to not only provide more insights into the current job market but also develop a strategic tool for career development in the rapidly evolving job market. Bachelor's degree 2024-05-17T04:40:34Z 2024-05-17T04:40:34Z 2024 Final Year Project (FYP) Ong, Y. S. (2024). Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176536 https://hdl.handle.net/10356/176536 en A3275-231 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Machine learning NLP Ong, Yuan Sheng Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG |
| title | Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG |
| title_full | Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG |
| title_fullStr | Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG |
| title_full_unstemmed | Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG |
| title_short | Analyzing job roles and skill description using NLP techniques (part 2: applying machine learning techniques) – collaboration with ARISE and SSG |
| title_sort | analyzing job roles and skill description using nlp techniques part 2 applying machine learning techniques collaboration with arise and ssg |
| topic | Engineering Machine learning NLP |
| url | https://hdl.handle.net/10356/176536 |
| work_keys_str_mv | AT ongyuansheng analyzingjobrolesandskilldescriptionusingnlptechniquespart2applyingmachinelearningtechniquescollaborationwithariseandssg |