Call Transcript NLP Analyzer

This project demonstrates an end-to-end NLP + LLM-style pipeline for analyzing unstructured call transcripts and converting them into structured business insights.

It is designed for Data Scientist / Machine Learning Engineer roles focused on:

• LLMs
• NLP
• Unstructured text data
• Call transcript analysis
• Text classification
• Sentiment analysis
• Summarization
• Insight extraction

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Business Problem

Many organizations receive thousands of customer service calls every month. These conversations contain important information such as:

• Customer pain points
• Product issues
• Service complaints
• Sentiment trends
• Escalation risk
• Operational improvement opportunities

However, call transcripts are unstructured text, making them difficult to analyze manually at scale.

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Solution

This project builds a lightweight NLP pipeline that analyzes call transcripts and extracts:

• Call summary
• Customer sentiment
• Main issue category
• Key phrases
• Escalation risk
• Actionable business insights

The project uses traditional NLP and machine-learning-style logic, with an optional LLM-ready structure that can be extended with OpenAI, Azure OpenAI, or other LLM APIs.

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Features

• Load call transcript data from CSV
• Clean and preprocess text
• Perform rule-based sentiment analysis
• Extract keywords and key phrases
• Classify calls into business categories
• Generate short summaries
• Flag high-risk calls
• Export structured results to CSV

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Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Regex
• NLP text preprocessing
• CSV-based data pipeline

Optional extensions:

• OpenAI API
• Azure OpenAI
• Embeddings
• RAG
• Vector databases such as FAISS or Pinecone

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Project Structure

call-transcript-nlp/
│
├── data/
│   └── sample_call_transcripts.csv
│
├── src/
│   ├── transcript_analyzer.py
│   └── utils.py
│
├── outputs/
│   └── analyzed_transcripts.csv
│
├── notebooks/
│   └── transcript_analysis_demo.ipynb
│
├── requirements.txt
├── .gitignore
└── README.md

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Sample Use Case

A company wants to analyze customer support calls to understand why customers are unhappy and which calls need follow-up.

This project processes call transcripts and produces structured outputs such as sentiment, issue category, escalation risk, key phrases, and summary.

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How to Run

1. Clone the repository

git clone https://github.com/SAINATHML/call-transcript-nlp.git
cd call-transcript-nlp

2. Install dependencies

pip install -r requirements.txt

3. Run the analyzer

python src/transcript_analyzer.py

4. View output

The processed file will be saved here:

outputs/analyzed_transcripts.csv

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Example Output Columns

• call_id
• transcript
• cleaned_text
• sentiment
• category
• escalation_risk
• key_phrases
• summary

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Future Improvements

• Add OpenAI / Azure OpenAI based summarization
• Add embedding-based semantic search
• Add RAG pipeline for querying transcript history
• Add dashboard using Streamlit
• Add model-based sentiment classification
• Add topic modeling using clustering

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Why This Project Matters

This project shows practical experience in applying NLP and AI to real-world unstructured text data. It demonstrates how raw call transcripts can be transformed into structured insights for business decision-making.

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Author

Vijayanand Goud
Data Scientist | Machine Learning Engineer | LLM | NLP | RAG