EmoXpt: Analyzing Emotional Variances in Human Comments and LLM-Generated Responses

TL;DR

This paper introduces EmoXpt, a sentiment analysis framework that compares emotional expressions in human comments and ChatGPT responses, revealing that LLM responses are more positive and cohesive.

Contribution

The study presents EmoXpt, a novel framework for analyzing emotional variances in human comments and LLM-generated responses, focusing on ChatGPT's emotional intelligence.

Findings

LLM responses are more positive than human comments.

ChatGPT responses are more cohesive and efficient.

EmoXpt effectively evaluates emotional differences in AI and human interactions.

Abstract

The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further understand the emotional intelligence of ChatGPT, we examine its responses to selected tweets, highlighting differences in sentiment between human comments and LLM-generated responses. We introduce EmoXpt, a sentiment analysis framework designed to assess both human perspectives on generative AI and the sentiment embedded in ChatGPT's responses. Unlike prior studies that focus exclusively on human sentiment, EmoXpt uniquely evaluates the emotional expression of ChatGPT. Experimental results demonstrate that LLM-generated responses are notably more efficient, cohesive, and consistently positive than human responses.