Reading
A Streamlit-based AI-driven resume analysis tool that uses TF-IDF and cosine similarity algorithms to help job seekers analyze the match between their resume and job descriptions, identify skill gaps, and provide ATS optimization suggestions.
Section 01
A Streamlit-based AI-driven resume analysis tool that uses TF-IDF and cosine similarity algorithms to help job seekers analyze the match between their resume and job descriptions, identify skill gaps, and provide ATS optimization suggestions.
Section 02
AI Resume Analyser is an open-source Streamlit-based web application that uses Natural Language Processing (NLP) technology to help job seekers analyze the match between their resume and target job positions. The project uses a Python tech stack with core dependencies including:
The advantage of this tech combination is its light weight, efficiency, and fully local operation—no need to call expensive third-party LLM APIs. All analysis is done locally, protecting user privacy while reducing usage costs.
Section 03
After users upload a PDF resume, the system automatically extracts the text content. This step seems simple, but it's the foundation of the entire analysis process. Accurate text extraction ensures that subsequent analysis is based on complete resume information.
Section 04
This is the core function of the project. The system uses TF-IDF (Term Frequency-Inverse Document Frequency) vectorization technology to convert resume text and job descriptions into numerical vectors, then calculates the match degree between the two using cosine similarity.
The advantage of TF-IDF is its ability to identify the importance of keywords—words that appear frequently in job descriptions but rarely in other documents are given higher weights. This means the system can not only count the number of keyword occurrences but also understand which skills are core requirements for the position.
Section 05
Based on keyword extraction technology, the system compares skill terms in the resume and job description to identify the skills the job seeker has and those they lack. This feature helps job seekers clarify their competitive advantages and areas for improvement.
Section 06
Modern recruitment processes commonly use Applicant Tracking Systems (ATS) for initial resume screening. AI Resume Analyser provides targeted ATS optimization suggestions to help job seekers adjust their resume format and content structure, increasing the probability of passing automatic screening.
Section 07
Based on the analysis results, the system generates specific resume improvement suggestions. These suggestions may include adding specific keywords, adjusting the way skills are described, optimizing project experience statements, etc.
Section 08
TF-IDF is a classic text feature extraction method, consisting of two parts:
TF-IDF value = TF × IDF
In this way, words that appear frequently in both the resume and prominently in the job description are given the highest weights—these words often represent core skill requirements.
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