Building a Sentiment Analysis Pipeline to Improve Customer Service Conversations with AI

4 min read4 hours ago

Introduction

Imagine a mid-sized e-commerce company managing customer inquiries through a chatbot. A frustrated customer contacts support about a failed order, and the conversation starts on a negative note. As the chatbot identifies the issue, offers a solution, and provides a discount, the customer’s tone gradually shifts to relief and satisfaction.

This project showcases a proof-of-concept sentiment analysis pipeline designed to monitor such interactions. By analyzing both AI and human messages, the tool identifies areas where AI helps resolve issues and where improvements might be necessary. The same solution can also analyze human-to-human conversations, such as those between live customer service agents and customers, providing insights into performance and customer sentiment shifts.

The pipeline provides actionable insights to help businesses:

Fine-tune conversational workflows.
Identify recurring pain points.
Measure sentiment trends across conversations.

As an early foundation, this implementation focuses on post-conversation analysis but demonstrates clear potential for real-time monitoring, cloud-based scalability, or domain-specific fine-tuning — offering a flexible path for future improvements.

Why This Tool Was Created

The primary goal of this project is to deliver a foundational solution for analyzing sentiment trends during both AI-human and human-to-human interactions. Businesses face common challenges in managing customer interactions:

Monitoring conversations to track emotional trends.
Identifying unresolved frustrations and gaps in service.
Highlighting successful de-escalation techniques or areas for improvement.

By providing sentiment summaries for entire sessions, this tool helps businesses make better decisions, improve AI training, and support agent performance reviews.

How It Works

Pipeline Workflow

The sentiment analysis pipeline processes conversation data and provides valuable insights into customer interactions. Here’s how it works:

1. Input

The pipeline takes structured CSV files containing customer service conversations. Each row in the CSV represents a message, with columns for:

session_id: Groups messages by conversation.
created_at: Timestamps each message.
message_type: Identifies whether the message is from AI or a human.
content: Stores the message text.

This structure ensures conversations are sorted chronologically and analyzed efficiently.

2. Processing

Parsing conversations into individual exchanges.
Performing sentiment analysis on each message using pre-trained models.
Aggregating sentiment trends across the session.

3. Output

The pipeline generates actionable insights, such as:

Trends in sentiment across conversations.
Highlights of recurring frustrations.
Summaries of successful or problematic exchanges.

Technologies Used

This tool leverages accessible, scalable technologies:

Python: Core development language.
Hugging Face Transformers: Pre-trained models for sentiment analysis.
Cardiff NLP models: Optimized for textual sentiment classification.
pandas: For processing structured data.

Challenges and Solutions

1. Skewed Sentiments in Technical Support

Customer support conversations often lean toward neutral or negative sentiments, as users typically reach out with problems.

Solution:

The pipeline aggregates sentiment scores across conversations to provide a holistic view of emotional trends.
It identifies whether a conversation begins negatively due to customer complaints but improves with effective responses.

2. Flexibility in Deployment

The tool is designed to run locally but can scale to meet the needs of businesses with large datasets.

Solution:

The modular design enables easy cloud deployment for larger operations or integration into existing systems.

Features of the Tool

Dynamic Label Mapping: Compatible with multiple sentiment analysis tasks and models.
Sentiment Summaries: Breakdowns by individual messages and session-level trends.
Flexible Integration: The tool integrates easily with other systems or workflows.
Modular Design: Functions as a standalone tool or part of a larger AI-driven process.

Future Possibilities

This sentiment analysis pipeline opens doors for further enhancements, including:

Real-Time Monitoring: Modifications like asynchronous processing could enable live sentiment tracking.
Fine-Tuning: Sentiment models trained on domain-specific datasets for greater accuracy.
Cloud Deployment: Adapting the tool as a SaaS solution for scalable sentiment analysis.

Code Highlights

Here’s an example of how the pipeline uses pre-trained models to analyze messages in batches:

from transformers import pipeline

# Load a pre-trained sentiment analysis model
sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")

# Analyze messages in a batch
def analyze_messages(messages):
    return sentiment_pipeline(messages, batch_size=32)

# Example messages
messages = ["The issue is unresolved.", "Thank you for fixing it!"]
results = analyze_messages(messages)
print(results)  # Output: [{'label': 'negative', ...}, {'label': 'positive', ...}]

For the complete implementation, check out the GitHub Repository:
🔗 SentimentAnalysis GitHub

Conclusion

This sentiment analysis pipeline demonstrates how AI can improve customer service by understanding emotional trends during interactions. Whether analyzing AI-human or human-to-human conversations, the tool provides actionable insights, highlights pain points, and measures satisfaction trends.

With its modular and scalable design, this tool sets the stage for real-time sentiment monitoring, domain-specific improvements, and cloud-based scalability. By addressing these challenges, businesses can refine workflows, reduce frustrations, and deliver better customer experiences.

Explore the code and see how you can adapt this tool to improve your support workflows:
🔗 GitHub Repository