"I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go...

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Bibliographic Details
Published in:arXiv.org 2022-12
Main Authors: Mubin Ul Haque, Dharmadasa, Isuru, Zarrin Tasnim Sworna, Rajapakse, Roshan Namal, Hussain, Ahmad
Format: Article
Language:English
Subjects:
Chatbots
Data mining
Disruptive innovation
Natural language processing
Qualitative analysis
Sentiment analysis
Software development
Online Access:Get full text
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Summary:Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that 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, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.
ISSN:2331-8422