Real-time chat application with sentiment analysis

This project is a real-time chat application built using ASP.NET Core as the backend and Blazor WebAssembly as the frontend. It integrates Azure SignalR Service for real-time communication and includes an optional integration of Azure Cognitive Services Text Analytics API for sentiment analysis of chat messages. The application is deployed on Azure Web App and uses Azure SQL Database for data storage.

Features

• Real-time chat: users can send and receive messages in real-time using Azure SignalR Service.
• Sentiment Analysis: optional integration with Azure Cognitive Services to analyze the sentiment of chat messages.
• Data Storage: chat messages and sentiment analysis results are stored in Azure SQL Database.
• UI Enhancements: messages are highlighted based on their sentiment (positive, negative, or neutral) using colors.

Screenshot

Real-time chat interface with sentiment analysis results.

Prerequisites

Before you begin, ensure you have the following:

• Azure Account: You need an Azure account to create and manage Azure resources.
• .NET SDK: Install the .NET SDK from here.

Setup and deployment

1. Clone the Repository

git clone https://github.com/hrytsenkooo/SignalRChat.git
cd SignalRChat

2. Create Azure resources

• Azure Web App: create an Azure Web App for hosting the ASP.NET Core application.
• Azure SignalR Service: create a SignalR Service instance for real-time communication.
• Azure SQL Database: create an Azure SQL Database for storing chat messages and sentiment analysis results.
• Azure Cognitive Services: create a Text Analytics resource for sentiment analysis.

3. Configure the application

Update the appsettings.json or create .env (according to example.env) file with your Azure resource connection strings and keys.

4. Database Migration

Run the following commands to apply database migrations.

dotnet ef database update

How to use

• Sign in: Enter your username to join the chat.
• Send messages: type your message in the input box and press Send.
• Data Storage: chat messages and sentiment analysis results are stored in Azure SQL Database.
• View sentiment: messages will be highlighted based on their sentiment (positive (green), negative (red), or neutral (gray)).

Happy Chatting! 🚀

Link on website