AI Interview Bot
1. Introduction
The AI Interview Bot is an interactive system designed to simulate HR interviews. It uses predefined questions and evaluates responses using Natural Language Processing (NLP) techniques. This project leverages sentiment analysis, text similarity, and keyword extraction for scoring answers.
2. Prerequisites
• Python: Install Python 3.x from the official Python
website.
• Required Libraries:
- nltk: Install using pip install nltk
- scikit-learn: Install using pip
install scikit-learn
- transformers: Install using pip
install transformers
• Pretrained Models: Install Hugging Face models for sentiment analysis
(optional).
• Basic knowledge of Python and NLP concepts.
3. Project Setup
1. Create a Project Directory:
- Name your project folder, e.g., `AI_Interview_Bot`.
- Inside this folder, create the Python script file (`interview_bot.py`).
2. Install Required Libraries:
Ensure nltk, scikit-learn, and transformers are installed using `pip`.
4. Writing the Code
Below is an example code snippet for the AI Interview Bot:
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from transformers import pipeline
# Predefined interview questions
questions = [
"Tell me about yourself.",
"What are your strengths?",
"Why do you want this
job?",
"Describe a challenging
situation and how you handled it."
]
# Initialize sentiment analysis model
sentiment_analyzer = pipeline("sentiment-analysis")
# Function to score responses
def analyze_response(question, response):
# Sentiment Analysis
sentiment =
sentiment_analyzer(response)[0]
sentiment_score = sentiment['score']
sentiment_label = sentiment['label']
# TF-IDF similarity (mock
implementation)
vectorizer = TfidfVectorizer()
tfidf_matrix =
vectorizer.fit_transform([question, response])
similarity_score = (tfidf_matrix *
tfidf_matrix.T).A[0, 1]
# Overall scoring
score = (similarity_score +
sentiment_score) / 2
print(f"Question:
{question}")
print(f"Response:
{response}")
print(f"Sentiment: {sentiment_label}
(Score: {sentiment_score:.2f})")
print(f"Similarity Score:
{similarity_score:.2f}")
print(f"Overall Score:
{score:.2f}
")
return score
# Main function for conducting an interview
def conduct_interview():
print("Welcome to the AI
Interview Bot!")
total_score = 0
for question in questions:
print(f"
{question}")
response = input("Your
response: ")
score =
analyze_response(question, response)
total_score += score
print(f"
Final Interview Score: {total_score / len(questions):.2f}")
# Run the Interview Bot
if __name__ == "__main__":
conduct_interview()
5. Key Components
• Predefined Questions: A list of common HR questions.
• Response Analysis: Uses sentiment analysis and text similarity for scoring.
• Interactive Console: Enables user interaction via a command-line interface.
6. Testing
1. Run the script:
python interview_bot.py
2. Provide responses to the interview questions and observe the scores.
7. Enhancements
• Advanced Scoring: Include keyword matching and semantic
analysis for better evaluation.
• GUI Development: Create a user-friendly interface for conducting interviews.
• Role-Specific Questions: Add tailored questions for different job roles.
8. Troubleshooting
• Low Scores: Adjust scoring logic or refine questions.
• Sentiment Issues: Use a more advanced sentiment analysis model or fine-tune
the existing one.
• Interaction Errors: Ensure proper handling of user inputs and edge cases.
9. Conclusion
The AI Interview Bot serves as a preliminary tool for interview preparation and evaluation. With enhancements, it can be utilized in educational or corporate settings for mock interviews and HR assessments.