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

• URL: http://arxiv.org/abs/2502.18513v1
• Date: Sat, 22 Feb 2025 17:00:42 GMT
• Title: Analyzing User Perceptions of Large Language Models (LLMs) on Reddit: Sentiment and Topic Modeling of ChatGPT and DeepSeek Discussions
• Authors: Krishnaveni Katta,
• Abstract summary: This study aims at analyzing Reddit discussions about ChatGPT and DeepSeek using sentiment and topic modeling.<n>Report mentions whether users have faith in the technology and what they see as its future.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Survey of User Interface Design and Interaction Techniques in Generative AI Applications [79.55963742878684]
  We aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. We also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.
  arXiv  Detail & Related papers  (2024-10-28T23:10:06Z)
• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces. This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• The Ethics of Advanced AI Assistants [53.89899371095332]
  This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user. We consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants.
  arXiv  Detail & Related papers  (2024-04-24T23:18:46Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Early ChatGPT User Portrait through the Lens of Data [9.497255050640344]
  We conduct a detailed analysis of real-world ChatGPT datasets with multi-turn conversations between users and ChatGPT. By understanding shifts in user demographics and interests, we aim to shed light on the changing nature of human-AI interaction.
  arXiv  Detail & Related papers  (2023-12-10T07:08:51Z)
• Language Models: A Guide for the Perplexed [51.88841610098437]
  This tutorial aims to help narrow the gap between those who study language models and those who are intrigued and want to learn more. We offer a scientific viewpoint that focuses on questions amenable to study through experimentation. We situate language models as they are today in the context of the research that led to their development.
  arXiv  Detail & Related papers  (2023-11-29T01:19:02Z)
• UltraFeedback: Boosting Language Models with Scaled AI Feedback [99.4633351133207]
  We present textscUltraFeedback, a large-scale, high-quality, and diversified AI feedback dataset. Our work validates the effectiveness of scaled AI feedback data in constructing strong open-source chat language models.
  arXiv  Detail & Related papers  (2023-10-02T17:40:01Z)
• What Twitter Data Tell Us about the Future? [1.7205106391379026]
  This study aims to investigate the futures projected by futurists on Twitter and explore the impact of language cues on anticipatory thinking. We present a compiled dataset of over 1 million publicly shared tweets by future influencers and develop a scalable NLP pipeline using SOTA models.
  arXiv  Detail & Related papers  (2023-07-20T14:02:47Z)
• Last Week with ChatGPT: A Weibo Study on Social Perspective Regarding ChatGPT for Education and Beyond [12.935870689618202]
  This study uses ChatGPT, currently the most powerful and popular AI tool, as a representative example to analyze how the Chinese public perceives the potential of large language models (LLMs) for educational and general purposes. The study also serves as the first effort to investigate the changes in public opinion as AI technologies become more advanced and intelligent.
  arXiv  Detail & Related papers  (2023-06-07T10:45:02Z)
• Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education [40.38809129759498]
  ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users.
  arXiv  Detail & Related papers  (2023-05-22T15:13:14Z)
• ChatGPT in education: A discourse analysis of worries and concerns on social media [1.4190701053683017]
  We analyzed Twitter data to identify key concerns related to the use of ChatGPT in education. Our study underscores the importance of responsible and ethical use of AI in education.
  arXiv  Detail & Related papers  (2023-04-29T22:08:42Z)
• A Categorical Archive of ChatGPT Failures [47.64219291655723]
  ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study.
  arXiv  Detail & Related papers  (2023-02-06T04:21:59Z)
• "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data [0.0]
  We conduct a mixed-method study using 10,732 tweets from early ChatGPT users. The majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT.
  arXiv  Detail & Related papers  (2022-12-12T12:41:24Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.