# Analyzing User Perceptions of Large Language Models (LLMs) on Reddit: Sentiment and Topic Modeling of ChatGPT and DeepSeek Discussions

arXiv: https://arxiv.org/abs/2502.18513 · HTML: https://mlsanity.com/paper/2502.18513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system](https://mlsanity.com/paper/2409.09989/) (2024)
- [AI Act and Large Language Models (LLMs): When critical issues and privacy impact require human and ethical oversight](https://mlsanity.com/paper/2404.00600/) (2024)
- [Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis](https://mlsanity.com/paper/2405.16810/) (2024)

## Built on by (most cited first, 1 of 1)

- [In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions](https://mlsanity.com/paper/2510.16173/) (2025)

## Abstract (arXiv)

While there is an increased discourse on large language models (LLMs) like ChatGPT and DeepSeek, there is no comprehensive understanding of how users of online platforms, like Reddit, perceive these models. This is an important omission because public opinion can influence AI development, trust, and future policy. This study aims at analyzing Reddit discussions about ChatGPT and DeepSeek using sentiment and topic modeling to advance the understanding of user attitudes. Some of the significant topics such as trust in AI, user expectations, potential uses of the tools, reservations about AI biases, and ethical implications of their use are explored in this study. By examining these concerns, the study provides a sense of how public sentiment might shape the direction of AI development going forward. The report also mentions whether users have faith in the technology and what they see as its future. A word frequency approach is used to identify broad topics and sentiment trends. Also, topic modeling through the Latent Dirichlet Allocation (LDA) method identifies top topics in users' language, for example, potential benefits of LLMs, their technological applications, and their overall social ramifications. The study aims to inform developers and policymakers by making it easier to see how users comprehend and experience these game-changing technologies.