Sentiment Analysis in ChatGpt Interactions: Unraveling Emotional Dynamics, Model Evaluation, and User Engagement Insights

ABSTRACT

The rapid evolution of AI, especially in natural language processing, has given rise to conversational AI models like ChatGPT, revolutionizing user engagement through text interactions. This study focuses on sentiment analysis within ChatGPT interactions, uncovering emotional dynamics and evaluating sentiment analysis models. Sentiment analysis holds significance in diverse domains, aiding in user sentiment interpretation. The research employs systematic methods, including data collection, analysis in the PyCharm IDE, and hypothesis testing. Findings reveal a mixed sentiment dataset, with user prompts tending to be more positive than ChatGPT responses. Sentiment during conversations exhibits dynamic shifts, and a Naive Bayes classifier-based model shows robust performance. This research enhances our understanding of sentiment analysis in ChatGPT, offering insights for refining conversational AI and user experiences in an AI-driven world.

KEYWORDS:

• Artificial Intelligence (AI) tools
• ChatGPT
• sentiment analysis
• Natural Language Processing (NLP)
• sentiment distribution
• Naive Bayes classifier
• evolving sentiment

Disclosure statement

No potential conflict of interest was reported by the author(s).