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International Research Journal of Engineering and Technology (IRJET) Volume: 11 Issue: 04 | Apr 2024

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e-ISSN: 2395-0056 p-ISSN: 2395-0072

SMARTHIRE: AN INTELLIGENT TALENT ACQUISITION SYSTEM WITH PREDICTIVE ANALYTICS Mr.G.Prabu1, A.Sudhan2, L.Johan3, M.Pandian4 1Assosiate Professor, Dept. of IT, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry 2,3,4 Student, Dept. of IT, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry

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Abstract-An Intelligent Talent Acquisition System with

Named Entity Recognition (NER): NER aims to identify and categorize named entities (e.g., names of persons, organizations, locations) within a text. It plays a crucial role in information extraction and entity linking tasks.

Predictive Analytics" is a cutting-edge project designed to revolutionize the recruitment process by leveraging the power of advanced machine learning and predictive analytics. This intelligent system aims to streamline and enhance talent acquisition for Human Resources (HR) professionals. SmartHire incorporates predictive analytics algorithms to evaluate resumes, predict candidate suitability, and optimize the recruitment workflow. By analyzing historical hiring data and identifying patterns, the system facilitates data-driven decision-making, enabling HR teams to identify the most promising candidates efficiently. The integration of predictive analytics not only accelerates the resume screening process but also ensures a more accurate and informed selection of candidates. SmartHire represents a significant advancement in talent acquisition technology, promising increased efficiency, objectivity, and overall excellence in the hiring process. Keywords—Talent acquisition, Human attrition, screening, streamline evaluation

Syntactic and Semantic Parsing: NLP involves analyzing the syntactic structure (grammar) and semantic meaning of sentences. This includes tasks like parsing sentences into syntactic trees and understanding relationships between words in a sentence. Word Embeddings and Semantic Similarity: NLP utilizes techniques to represent words as dense vectors in a continuous vector space, known as word embeddings. These embeddings capture semantic similarities between words and enable algorithms to understand the context of words in a document. Machine Translation: NLP enables the translation of text from one language to another using machine translation models. This involves understanding the meaning of the source language text and generating equivalent text in the target language.

Resources,

1.INTRODUCTION

Sentiment Analysis and Opinion Mining: NLP techniques are applied to analyze and understand the sentiment or opinion expressed in text data, which has applications in social media monitoring, customer feedback analysis, and market research.

1.1 NATURAL LANGUAGE PROCESSING (NLP) Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that deals with the interaction between computers and humans through natural language. Its primary goal is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. Here's an elaborate overview of NLP, its scope, and applications:

Question Answering Systems: NLP facilitates the development of question answering systems that can understand and respond to questions posed in natural language. These systems often rely on techniques like information retrieval, text summarization, and reasoning.

1.2 SCOPE OF NLP: Tokenization and Text Preprocessing: NLP involves breaking down text into smaller units such as words, phrases, or sentences, known as tokenization. It also includes text preprocessing tasks like removing punctuation, stop words, and normalizing text (e.g., converting all letters to lowercase).

Text Generation and Summarization: NLP techniques enable the generation of human-like text and summaries based on input data. This includes tasks like text summarization, dialogue generation, and story generation.

Part-of-Speech Tagging: This task involves assigning grammatical categories (e.g., noun, verb, adjective) to each word in a sentence, which helps in understanding the syntactic structure of the text.

Virtual Assistants: NLP powers virtual assistants like Siri, Alexa, and Google Assistant, enabling users to interact with devices and applications using natural language commands.

1.3 APPLICATIONS OF NLP:

Impact Factor value: 8.226

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