Custom Model Development for Sentiment Analysis

Client Situation

A retail vendor wanted to build a Sentiment Analysis application to analyze customer feedback and derive actionable insights. While existing pre-trained LLMs could provide basic sentiment detection, they lacked the ability to:

• Understand retail-specific nuances in customer reviews and feedback.
• Accurately classify granular sentiments such as frustration, satisfaction, or product-specific complaints.
• Process large volumes of unstructured customer data efficiently.
• Provide customized outputs aligned with the client’s specific business goals and customer engagement strategy.

The client needed a customized LLM tailored to their data to improve accuracy and deliver meaningful sentiment insights.

Our Solution

We developed a custom sentiment analysis model fine-tuned specifically on the client’s retail customer feedback data:

• Data Collection and Preparation:
  • Collaborated with the client to gather and clean historical customer reviews, support tickets, and survey responses.
  • Pre-processed unstructured text to make it suitable for fine-tuning.
• Custom LLM Fine-Tuning:
  • Selected a lightweight, open-source LLM as the base model for cost and performance optimization.
  • Fine-tuned the model using retail-specific feedback datasets to improve sentiment detection accuracy.
  • Added classification layers to identify granular sentiments such as:
    • Positive/negative tone
    • Product quality issues
    • Delivery delays and service complaints
• Model Optimization:
  • Optimized the model to ensure faster inference times while maintaining high accuracy.
  • Deployed the solution on scalable infrastructure to handle real-time customer data.
• Custom Outputs and Insights:
  • Designed outputs to provide sentiment trends, key topics, and actionable insights in user-friendly dashboards.
  • Integrated visual analytics to highlight trends over time and areas needing attention.

Outcome

• Delivered a custom fine-tuned LLM tailored to the retail domain and customer-specific feedback data.
• Improved sentiment analysis accuracy by over 30% compared to off-the-shelf models.
• Enabled granular insights into customer pain points, satisfaction levels, and emerging issues.
• Reduced processing time, allowing the client to analyze thousands of feedback records in minutes.
• Empowered business teams with actionable insights to improve customer satisfaction, product quality, and service delivery.

Client Testimonial

“The custom sentiment analysis model built by GenAI Protos has been a game-changer for us. It not only gave us more accurate insights into customer feedback but also helped us act faster on areas where improvements were needed.”