V-SENSE
Email Sentiment Analysis
This project is designed to analyse client emails and categorize their sentiments using a fine-tuned machine-learning model. The pipeline consists of multiple modules for data filtering, cleaning, classification, and sentiment analysis. The results are visualized using Power Bi for better insights.
Objective
The primary objective of this project is to automatically process client emails, classify their sentiment, and provide insightful visualizations for better decision-making.
AI-Powered Intelligence
V-SENSE leverages advanced Natural Language Processing and Machine Learning to intelligently filter, categorize, and analyze client emails. The system ensures your team focuses only on what truly matters—real customer communication.
Business-Centric Insights
By turning unstructured email data into clear, actionable dashboards, V-SENSE empowers teams to track customer satisfaction, identify key trends, and respond proactively—improving service quality and operational efficiency.
How It Works
V-SENSE operates through a streamlined, intelligent pipeline designed to convert raw client email data into actionable business insights. From retrieving emails to presenting them in an intuitive dashboard, each step in the process is carefully designed for accuracy, clarity, and speed. Here’s a detailed look into how the system works end-to-end:
Email Retrieval
Client emails are securely retrieved from Azure-based APIs. These emails form the raw data input for the system and may include various types of communication such as inquiries, feedback, newsletters, and automated messages.
Smart Filtering
Before storing emails in the database, the frontend applies intelligent filters to remove irrelevant content such as newsletters, promotional emails, no-reply responses, and emails with minimal content. This step ensures that only meaningful communications proceed to the next phase.
Data Cleaning
The filtered emails undergo a thorough cleaning process using natural language processing techniques. This step eliminates duplicate entries, escape characters, bracketed texts, and unnecessary headers or footers to prepare clean and structured data for analysis.
Email Classification
The cleaned email content is then categorized into “Regular” or “Notification” emails. Regular emails include personal or work-related communication, while Notification emails refer to system alerts or newsletters. This is done using a fine-tuned Gradient Boosted Machine Learning model.
Sentiment Analysis
All Regular emails are further analyzed to detect sentiment — positive, neutral, or negative. V-SENSE leverages a pre-trained Hugging Face model to assess the emotional tone of each message, helping businesses understand client mood and urgency.
Data Visualization
Finally, the analyzed data is transformed into rich, interactive dashboards using tools like Power BI or Tableau. These visualizations provide real-time insights into client sentiment, communication trends, and areas of improvement across the business.
Key Use Cases
Customer Sentiment Monitoring
Proactively identify unhappy clients, analyze feedback trends, and improve service quality.
Email Routing & Prioritization
Classified emails can be forwarded to relevant departments instantly, speeding up responses.
Spam & Noise Reduction
Say goodbye to clutter with automatic removal of irrelevant or system-generated content.
Business Intelligence Dashboards
Visualize volume trends, sentiment shifts, and customer pain points for strategic decision-making.
Let’s Work Together
We look forward to start a success journey with you. Please do write to us how can we help you.