ChatGPT API and BERT NLP

<b>input_prompts.csv</b> provides the inputs for the ChatGPT API (countries and their respective prompts).<b>topic_consolidations.csv </b>contains the 4,018 unique topics listed across all ChatGPT responses to prompts in our study and their corresponding cluster labels after applying K-means++ clustering (n = 50) via natural language processing with Bidirectional Encoder Representations from Transformers (BERT). ChatGPT response topics come from both versions (3.5 and 4) over 10 iterations each (per each country).<b>ChatGPT_prompt_automation.ipynb</b> is the Jupyter notebook of Python code used to run the API to prompt ChatGPT and gather responses.<b>topic_consolidation_BERT.ipynb</b> is the Jupyter notebook of Python code used to process the 4,018 unique topics gathered through BERT NLP. This code was adapted from Vimal Pillar on Kaggle (https://www.kaggle.com/code/vimalpillai/text-clustering-with-sentence-bert).