Automated Candidate Interview Sentiment Analytics Platform

About the Project

Inspiration

Technical interviews are an important part of recruitment, but evaluating candidates manually can take a lot of time. Different interviewers may also evaluate the same answer differently. We wanted to develop a platform that helps make interview evaluation more organized by analyzing candidates' responses, identifying sentiment, and assessing their technical knowledge.

What We Built

We developed an Automated Candidate Interview Sentiment Analytics and Technical Competency Evaluation Platform that analyzes interview responses and generates an evaluation report. The platform aims to classify responses into positive, neutral, and negative sentiment categories and assess technical answers based on relevant concepts and expected answers. It provides an overview of technical competency, communication patterns, strengths, weaknesses, and areas for improvement.

How We Built It

We planned the project by identifying its main objectives, defining the system architecture, and dividing the work among team members. We explored frontend and backend development, database management, natural language processing (NLP), and sentiment analysis. We designed the database to store user information, interview questions, candidate responses, and evaluation results. Our development approach combines a user-friendly interface, backend processing, and AI/ML techniques to support automated evaluation.

What We Learned

Through this project, we learned how software development and AI/ML can be combined to address a real-world recruitment challenge. We gained a better understanding of sentiment analysis, NLP, database design, system architecture, and teamwork. We also learned the importance of presenting evaluation results clearly so that users can understand a candidate's performance.

Challenges We Faced

One of our main challenges was planning how to evaluate technical answers while considering differences in wording and meaning. Another challenge was understanding how to integrate sentiment analysis with technical competency evaluation and store the results in a structured format. We also had to divide responsibilities among team members and plan the integration of different modules.

Future Scope

In the future, we aim to improve the accuracy of technical answer evaluation, support more interview domains, and provide more detailed feedback. We also plan to improve the platform's usability and evaluate its performance with diverse sample responses. The platform is intended to assist human interviewers rather than replace their judgment.