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Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations

Ratanond Koonchanok, Yanling Pan, Hyeju Jang

TL;DR

This study analyzes public attitudes toward ChatGPT on Twitter over seven months post-release, using XLM-T for sentiment analysis and BERTopic for topic modeling, augmented by occupation extraction from user bios. It finds a predominantly neutral-to-positive sentiment that improves over time, with key topics including Education, Bard, Search Engines, OpenAI, Marketing, and Cybersecurity; topic prominence shifts monthly in response to events. Occupation analysis shows arts and entertainment users tweeting most about ChatGPT and reveals occupation-specific topic patterns, suggesting users discuss topics tied to their professions. The work offers actionable context for developers and policymakers to understand public perception and to tailor communication and design to different occupational groups. Overall, the approach provides a nuanced, longitudinal view of public sentiment and discourse around a transformative AI system.

Abstract

ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generation tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we investigated public attitudes towards ChatGPT by applying natural language processing techniques such as sentiment analysis and topic modeling to Twitter data from December 5, 2022 to June 10, 2023. Our sentiment analysis result indicates that the overall sentiment was largely neutral to positive, and negative sentiments were decreasing over time. Our topic model reveals that the most popular topics discussed were Education, Bard, Search Engines, OpenAI, Marketing, and Cybersecurity, but the ranking varies by month. We also analyzed the occupations of Twitter users and found that those with occupations in arts and entertainment tweeted aboutChatGPT most frequently. Additionally, people tended to tweet about topics relevant to their occupation. For instance, Cybersecurity is the most discussed topic among those with occupations related to computer and math, and Education is the most discussed topic among those in academic and research. Overall, our exploratory study provides insights into the public perception of ChatGPT, which could be valuable to both the general public and developers of this technology.

Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations

TL;DR

This study analyzes public attitudes toward ChatGPT on Twitter over seven months post-release, using XLM-T for sentiment analysis and BERTopic for topic modeling, augmented by occupation extraction from user bios. It finds a predominantly neutral-to-positive sentiment that improves over time, with key topics including Education, Bard, Search Engines, OpenAI, Marketing, and Cybersecurity; topic prominence shifts monthly in response to events. Occupation analysis shows arts and entertainment users tweeting most about ChatGPT and reveals occupation-specific topic patterns, suggesting users discuss topics tied to their professions. The work offers actionable context for developers and policymakers to understand public perception and to tailor communication and design to different occupational groups. Overall, the approach provides a nuanced, longitudinal view of public sentiment and discourse around a transformative AI system.

Abstract

ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generation tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we investigated public attitudes towards ChatGPT by applying natural language processing techniques such as sentiment analysis and topic modeling to Twitter data from December 5, 2022 to June 10, 2023. Our sentiment analysis result indicates that the overall sentiment was largely neutral to positive, and negative sentiments were decreasing over time. Our topic model reveals that the most popular topics discussed were Education, Bard, Search Engines, OpenAI, Marketing, and Cybersecurity, but the ranking varies by month. We also analyzed the occupations of Twitter users and found that those with occupations in arts and entertainment tweeted aboutChatGPT most frequently. Additionally, people tended to tweet about topics relevant to their occupation. For instance, Cybersecurity is the most discussed topic among those with occupations related to computer and math, and Education is the most discussed topic among those in academic and research. Overall, our exploratory study provides insights into the public perception of ChatGPT, which could be valuable to both the general public and developers of this technology.
Paper Structure (24 sections, 6 figures, 5 tables)

This paper contains 24 sections, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Dataset
  5. Data Cleaning
  6. Sentiment Analysis
  7. Topic Modeling
  8. Occupation Extraction and Analysis
  9. Results
  10. Sentiment Analysis
  11. Topic Modeling Analysis
  12. Most frequent topics
  13. Sentiments for selected topics
  14. Occupation Analysis
  15. Identified Occupations
  16. ...and 9 more sections

Figures (6)

  • Figure 1: Overview of data cleaning and preprocessing
  • Figure 2: Distribution of positive, neutral, and negative sentiments of the full dataset predicted by XLM-T
  • Figure 3: Sentiment distribution by month from December 2022 to June 2023
  • Figure 4: Percentage distribution of negative, neutral, and positive sentiments for selected topics
  • Figure 5: Changes in sentiment for selected topics after 12 weeks
  • ...and 1 more figures