A sentiment analysis of YouTube videos from donor-conceived people, utilizing artificial intelligence (ChatGPT)

What is the tone of donor-conceived people (DCP) towards their donor-conceived status in YouTube videos assessed for sentiment analysis via ChatGPT, a natural language-processing tool?

A cross-sectional analysis of YouTube videos using the keyword 'donor conceived' was performed by two independent reviewers. Videos were selected for inclusion if they described DCPs' opinions, stories or emotions about their life experiences. Reviewers categorized the sentiments of the videos as neutral, positive, negative or mixed. The auto-generated English-language YouTube transcript for each video was then inputted into ChatGPT (GPT-4o model) using the following prompt: 'Given this text, what is the sentiment conveyed? Is it positive or negative? Text: {transcript}'. Sentiment results generated via ChatGPT were categorized as neutral, positive, negative or mixed. Chi-squared goodness-of-fit and independence tests were performed to assess the observed proportions of sentiments compared with the expected distribution.

Fifty YouTube videos were identified. The median video length was 6.91 min and the median number of views was 2217.5. Via ChatGPT, 44% of videos were determined to have a positive sentiment, 28% were negative, 24% were mixed and 4% were neutral, with a significant difference in the observed distribution of sentiments from expected (Χ² [3, n = 50] = 16.240, P = 0.001). When compared with two human reviewers, ChatGPT sentiment results had 90% concordance with at least one reviewer.

ChatGPT shows a strong performance in analysing YouTube video transcript sentiments when compared with human reviewers. The sentiments of DCP towards their donor-conceived status in YouTube videos have a positive leaning when analysed by ChatGPT.