Paper Key : IRJ************578
Author: Miss. Shubhangi Ram Kale,Mr. Tanmay Kolase,Mr. Yash Koli
Date Published: 02 May 2024
Abstract
Abstract:-This study adopted machine learning- and text mining technology-based artificial intelligence and current big data technology to analyze the trendiness of online discussion.Developing a system that can be applied in large job fairs, where numerous job applicants seek to match with the maximum of job vacancies provided by companies possible. The developed system conducts personal competitiveness analysis, personality trait analysis, and gives job va-cancy recommendations according to the electronic resumes job applicants submit. The results demonstrated that the designed system identified the current demand on talent-seeking and quickly presented candidate rankings for a specific position, thereby fulfilling the needs of both job-hunting candidates and talent-seeking recruiters.Introduction:-Corporate companies and recruitment agencies process numerous resumes daily. This is no task for humans. An automated intelligent system is required which can take out all the vital information from the unstructured resumes and transform all of them to a common structured format which can then be ranked for a specific job position. Parsed information includes (name,email address, phone number, work experiences, education, hobbies, interests, achievements,certifications, projects) keywords and finally the cluster of the resume (ex: Web Development,Data Science etc.). The parsed information is then stored in a database (MySQL in this case)for later use. Unlike other unstructured data (ex: email body, web page contents, etc.), resumes are a bit structured. Information is stored in discrete sets. Each set contains data about the persons contact, work experience or education details. In spite of this, resumes are difficult to parse. This is because they vary in types of information, their order, writing style, etc. To parse the data from different kinds of resumes effectively and efficiently, the model must not rely on the order or type of data. To solve this tedious process our tool comes into action whichmakes the process fast, easy and reliable. Using NLP Techniques, it extracts keywords from the resume and use it for predictions, recommendation and analytical representation.