AutoThemeGenerator 0.1.5

Performing thematic analysis with OpenAI's GPT-4 models

AutoThemeGenerator is a package that allows you to perform thematic analysis in qualitative studies using OpenAI's GPT models.

User inputs

Users are only required to specify the folder location where their interview transcripts are stored. Accepted formats of transcripts include PDF, .docx, and .txt (prefered). AutoThemeGenerator assumes that each document is a transcript of one interviewed participant.

Requirements

Required packages

To use AutoThemeGenerator, you are required to have the following packages installed:

• openai

• docx

• tqdm

• nltk

• nltk.tokenize (submodule of nltk)

• python-docx

• textract

• requests

• zipfile (Python standard library)

• shutil (Python standard library)

• json (Python standard library)

• pprint (Python standard library)

If you do not have these packages installed in python, you can do the following:

pip install openai==1.12.0 python-docx docx tqdm nltk textract requests

OpenAI API key

You also need an OpenAI key to be able to use this package. If you do not have one, you can apply for an OpenAI API key at platform.openai.com/api-keys.

Installation

To install in python, simply do the following:

pip install AutoThemeGenerator

Quick Start

Here we provide a quick example on how you can execute AutoThemeGenerator to conveniently perform qualitative analysis from your transcript. For details towards each of the package's functions and parameters, refer to the documention.

from AutoThemeGenerator import analyze_and_synthesize_transcripts



# Specify the folders containing your transcript

# This is the folder containing transcripts in .docx, .PDF or .txt format

directory_path = "my_transcript_folder"

# specify your OpenAI API key

api_key = "<insert your API key>"

# specify the folder you wish to save your themes. 

save_results_path = "folder_of_my_saved_results"



# specify the context of your study

context = (

    "Physical inactivity is a major risk factor for developing several chronic illness. "

    "However, university students and staff in the UK are found to be more physically inactive "

    "compared the general UK population. "

    )

# specify your research questions

research_questions = (

    "This study seeks to understand the barriers and enablers "

    "of physical activity (PA) among university staff and students in "

    "the UK under the university setting, using the Theoretical "

    "Domain Framework (TDF) to guide the investigation. "

    )

# specify your survey script

survey_script = (

    "Knowledge\n "

    "What do you know about physical activity? How might you define physical activity? "

    "... ..." # note: truncated to save space

    "... ..." 

    )



# Analyze and synthesize transcripts

initial_themes, individual_synthesized_themes, overall_synthesized_themes = \

analyze_and_synthesize_transcripts(

    directory_path = directory_path, context = context,

    research_questions = research_questions, script = survey_script,

    api_key = api_key, save_results_path = save_results_path)



# display your study-level themes

print(overall_synthesized_themes)

You can now view the themes in the form of a topic sentence, a detailed explaination and a relevant quote

Citation

Y Yang, C Alba, W Xi, M Li, C Wang, A Jami, R An. "GPT Models Can Perform Thematic Analysis in Public Health Studies, Akin to Qualitative Researchers" Working paper.

Questions?

Contact me at [e-mail]