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SwarBhaav - Transforming Calls into Actionable Feedback

Table of Contents

Introduction

The Sentiment Analysis of Incoming Calls on Helpdesk project is a comprehensive solution tailored for analyzing the sentiment of incoming calls in helpdesks, call centers, and customer services. With the ever-increasing volume of customer interactions in these domains, understanding customer sentiments expressed during phone conversations is crucial for improving customer service delivery.

Features

  • Dual Dashboards: The project offers separate dashboards for rookies and managers, each with distinct access controls.

  • Chat Analysis: Analyze chat conversations to provide summaries, sentiment scores, and key positive and negative words. Users can specify date ranges for analysis.

  • Swar or Audio Sentiment Analysis: Utilize the Librosa model to classify Swar recordings into one of seven different emotions, enhancing the depth of sentiment analysis.

  • Reporting: Generate comprehensive reports and store the results in a MongoDB database for users to review whenever necessary, facilitating data-driven decision-making.

Demo

For a live demo of the project, visit Demo Link.

Tech Stack

  • Frontend:

    • Next.js: React framework for building user interfaces.
    • Tailwind CSS: Utility-first CSS framework.

  • Backend:

    • Node.js: JavaScript runtime for server-side development.
    • FastAPI: Modern, fast (high-performance) web framework for building APIs.

  • Sentiment Analysis:

    • Librosa: Python library for audio and music analysis.
    • Transformers: State-of-the-art natural language processing models.

  • Data Sources:

    • Swar Dataset
    • WhatsApp Chats

Installation

  1. Clone the repository:

    git clone https://github.com/Archit1706/SwarBhaav.git

  2. Install dependencies for both the frontend and backend:

    # Navigate to the frontend directory
cd frontend
npm install

# Navigate to the backend directory
cd ../backend
pip install -r requirements.txt

  3. Configuration:

    • Set up environment variables and configuration files as needed for your development and production environments.

  4. Start the development server:

    # In the frontend directory
npm run dev

# In the backend directory
uvicorn main:app --reload

Usage

  • Access the application by visiting the appropriate URL in your browser.
  • Log in with your credentials to access the dashboard.
  • Explore the chat analysis, Swar sentiment analysis, and reporting features based on your user role.