chat-analysis 🤖

In this project you will find a small example of how you can leverage data science and simple scripting to come up with interesting insights about your business 🦄

Setup

You will need

• Postgresql
• Node.js
• Jupyter Notebook

Installation

• Modify the settings on config/config.json to match your local environment. This includes database name, user, etc

• Create a database matching the settings you've specified

• $ npm install

• $ node_modules/sequelize-cli/bin/sequelize db:migrate

• Now your project should be ready.

Usage

• Load transcripts into the data folder - they should be an XLSX file matching the format our customer agent service has provided - feel free to ping @mariogintili for details on this if need be.

• run $ node app/sync.js

Contributing

• Check out from master

• Submit a PR

Usage 🔮

As defined on on the conversation model the data is stored in one table, following this structure:

  id: {
    type: INTEGER,
    primaryKey: true,
    autoIncrement: true
  },
  startTime: STRING,
  endTime: STRING,
  interactionId: STRING,
  agentId: STRING,
  queue: STRING,
  sourceCategory: STRING,
  customerName: STRING,
  conversation: TEXT,
  exitSurvey: TEXT,
  createdAt: {
    type: DATE
  },
  updatedAt: {
    type: DATE
  }

However, one thing to note is that the conversation field is a text field storing a serialized and valid JSON object. This is made out of ease of segmentation based the authors of the messages for a given conversation. Which follows this schema:

[
	{
		isSupport: true,
		message: "Hello angry client, how may I help you?"
	},
	{
		isSupport: false,
		message: "I am very angry at your service"
	}
]

⚠️ This field is a string - if you want to make use of the structure you'll need to parse this as JSON. JSON.parse