Comparative Analysis On The Impact of GPT On Human Thinking Using Sentiment Analysis

This research article examines the effects of GPT (Generative Pre-trained Transformer) models on human thought processes and emotions, specifically looking at changes in sentiment and themes in user feedback. Using a multi-step approach that includes comparative sentiment evaluation, word-level sentiment examination, and thematic modelling, the study assesses how users’ views and cognitive articulations evolve before and after their interaction with GPT. Sentiment evaluations were performed via VADER and TextBlob to ensure thoroughness and validation of polarity and subjectivity ratings. The thematic analysis revealed shifting trends in trust, doubt, and hands-on engagement with AI-created material. Statistical analyses, such as Tukey’s HSD, were utilized to determine the relevance of sentiment differences among various user demographics, identifying significant variations linked to age. By combining sentiment trend observations, word co-occurrence networks, and comparisons of polarity and subjectivity scores, the research provides a detailed perspective to gauge the nuanced yet quantifiable impact of GPT on human cognition and emotional perspectives. These results enhance the overall comprehension of human-AI relationships and their significance for digital interaction, AI acceptance, and cognitive changes.