Moderator Dashboard

Table of Contents

• Introduction
• Getting Started
• Build and Test
• Contribute

Introduction

The main goal of this project is to create a dashboard for a moderator who is in responsible for monitoring employee comments that are posted to campagins on the Zenya Boost platform. The moderator dashboard harnesses AI technologies like:

💭 Sentiment analysis API to gain insight into the overall reaction to the campaign aswell as individual comments.
❓ Question detection API to help the moderator quickly identify questions that need to be answered.
👮‍♀️ Content moderation API to help the moderator quickly identify toxicity/innapropriate content like hate, self harm, sexual and violent content.
🇬🇧 Translation API to translate non-Egnlish comments into English so that the other AI services can be applied to them.

Getting Started

1. Installation process
2. Software dependencies
3. Latest releases
4. API references

To install the program, you need to open any command-line interface (CLI) tools you prefer in the root of this repository. Make sure docker desktop is runnning in your local machine. Then run these commands:

# Build and run the backend
docker compose build
# Run the backend
docker compose up

You can go to localhost:3000 to check out the moderator dashboard.

Build and Test

To build the backend, follow these steps:

1. Navigate to the BoostModeratorDashboardBackend directory.
2. Open the BoostModeratorDashboard.sln file with Visual Studio 2022.
3. Right-click on the BoostModeratorDashboard solution.
4. Select Build Solution.

To test the backend:

1. Locate the BoostModeratorDashboardAPITests project inside 2. the solution.
2. Right-click on the BoostModeratorDashboardAPITests project.
3. Choose Run Tests.

Contribute

In our branch management strategy, we adopt feature branches for development. Each branch is dedicated to a specific feature. Once a feature is complete, a pull request is initiated towards the development branch, requiring approval from at least one team member. When the development branch is ready for integration with the main branch, a pull request is created and must be approved by at least one other team member.

To distinguish between branches when using a single Azure DevOps account, we add a prefix to the branch name in the format name/feature_name (snake case for feature). However, if a team member has their own unique Azure DevOps account, there is no need to include the prefix.