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

arXiv: https://arxiv.org/abs/2501.06597 · HTML: https://mlsanity.com/paper/2501.06597/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- ["I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data](https://mlsanity.com/paper/2212.05856/) (2022)
- [Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usage](https://mlsanity.com/paper/2305.09537/) (2023)

## Built on by (most cited first, 3 of 3)

- [LLM-ProS: Analyzing Large Language Models' Performance in Competitive Problem Solving](https://mlsanity.com/paper/2502.04355/) (2025)
- [EmoBang: Detecting Emotion From Bengali Texts](https://mlsanity.com/paper/2511.07077/) (2025)
- [SECite: Analyzing and Summarizing Citations in Software Engineering Literature](https://mlsanity.com/paper/2601.07939/) (2026)

## Abstract (arXiv)

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.