OpenAI-API: Qualitative Data Analysis Notebook

This repository provides prompting guidelines and strategies for dealing with qualitative data using OpenAI's API. It includes examples, best practices, and Jupyter Notebooks to help users effectively interact with OpenAI's language models for qualitative data analysis.

This is a on process Jupyter Notebook for data analysis of qualitative information, such as opinions given in a scientifi event form.

Future Work

• Web scraping and consultation on major social networks to leverage the knowledge of the context in which these opinions evolve
• Insert these insights in the bigger institution scenario (relate past changes with consequences)
• Propose a roadmap that aligns with the discovered environment and goals

Table of Contents

• Introduction
• Features
• Setup and Installation
• Usage
• Examples

Introduction

Qualitative data often requires thoughtful analysis and interpretation. This repository is a collection of guidelines, examples, and notebooks that demonstrate how OpenAI's API can be leveraged to:

• Summarize qualitative data.
• Extract themes or patterns.
• Perform sentiment or discourse analysis.
• Generate insights or narratives.

Whether you're a researcher, student, or professional working with qualitative data, this repository aims to provide you with tools and techniques to enhance your workflows.

Features

• Prompting Guidelines: Best practices for crafting effective prompts for qualitative data tasks.
• Interactive Notebooks: Jupyter Notebooks with ready-to-use examples and workflows.
• Use Cases: Demonstrations of various qualitative data analysis techniques.
• Customizable Templates: Prompt templates for different tasks, such as summarization, theme extraction, and sentiment analysis.

Setup and Installation

Prerequisites

• Python 3.8 or newer.
• Jupyter Notebook installed.
• OpenAI API key. You can get an API key by signing up at OpenAI.

Installation Steps

1. Clone the repository:

   git clone [https://github.com/AMorQ/OpenAI-API.git](https://github.com/AMorQ/OpenAI-API.git)
   cd OpenAI-API

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Launch Jupyter Notebook:

   jupyter notebook

Usage

Set Up Your API Key:

Before running the notebooks, set your OpenAI API key as an environment variable:

export OPENAI_API_KEY="your-api-key-here"

Examples

Here are some of the example tasks included in this repository:

• Summarizing Interview Transcripts: Extract key points and themes from lengthy interview transcripts.
• Thematic Analysis: Generate themes from qualitative datasets such as open-ended survey responses.
• Sentiment and Emotion Analysis: Assess the sentiment or emotional tone of qualitative text data.
• Discourse or Narrative Analysis: Identify narrative structures or discourse patterns within qualitative content.