To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Analysis of LinkedIn Jobs for Finding High Demand Job Trends Using Text Processing Techniques |
|
Author |
Abdul Karim Kazi, Muhammad Umer Farooq, Zainab Fatima, Saman Hina, Hasan Abid |
| Citation |
Vol. 22 No. 10 pp. 223-229 |
|
Abstract |
LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Na?ve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions |
|
Keywords |
Job posting; Natural Language Processing; Text Processing |
|
URL |
