Using Sentiment Analysis for Comparing Attitudes between Computer Professionals and Laypersons on the Topic of Artificial Intelligence

被引:5
|
作者
Wang, Xueying [1 ]
机构
[1] Ningxia Univ, West Helan Rd 539, Yinchuan, Ningxia, Peoples R China
关键词
Tweets; AI; Lexicon; Polarity Classification; Public Attitudes; Scientific Knowledge; Layperson; Computer Professional;
D O I
10.1145/3342827.3342829
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most research in investigating computer professionals and laypersons. attitudes toward artificial intelligence (AI) are limited to online or offline surveys. This paper analyzes computer professionals. and laypersons. attitudes toward AI by using a sentiment lexicon developed by Wilson et al. To explore whether there is a correlation between the occupation categories (computer-related versus non-computer-related occupations) and people.s attitudes toward artificial intelligence, I conducted a polarity classification of over 0.6 million tweets containing references to "AI", "artificial intelligence", or both. The result did not provide evidence of a relationship between public attitudes toward AI and the occupation categories. In the end, several future directions in the data collection and the data analysis are discussed.
引用
收藏
页码:5 / 8
页数:4
相关论文
共 50 条
  • [1] The development of a competence framework for artificial intelligence professionals using probabilistic topic modelling
    Brauner, Sonja
    Murawski, Matthias
    Bick, Markus
    JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT, 2025, 38 (01) : 197 - 218
  • [2] Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach
    Dumbach, Philipp
    Schwinn, Leo
    Loehr, Tim
    Do, Phi Long
    Eskofier, Bjoern M.
    EVOLUTIONARY INTELLIGENCE, 2024, 17 (04) : 2145 - 2166
  • [3] Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach
    Ocal, Ayse
    IEEE ACCESS, 2024, 12 : 182386 - 182409
  • [4] Public attitudes toward higher education using sentiment analysis and topic modeling
    Göçen, Ahmet
    Ibrahim, Mahat Maalim
    Khan, Asad Ul Islam
    Discover Artificial Intelligence, 2024, 4 (01):
  • [5] Enhancing the Classification Accuracy in Sentiment Analysis with Computational Intelligence Using Joint Sentiment Topic Detection with MEDLDA
    Kalaivaani, P. C. D.
    Thangarajan, R.
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2020, 26 (01): : 71 - 79
  • [6] Business boosting through sentiment analysis using Artificial Intelligence approach
    Alim Al Ayub Ahmed
    Sugandha Agarwal
    IMade Gede Ariestova Kurniawan
    Samuel P. D. Anantadjaya
    Chitra Krishnan
    International Journal of System Assurance Engineering and Management, 2022, 13 : 699 - 709
  • [7] Business boosting through sentiment analysis using Artificial Intelligence approach
    Ahmed, Alim Al Ayub
    Agarwal, Sugandha
    Kurniawan, IMade Gede Ariestova
    Anantadjaya, Samuel P. D.
    Krishnan, Chitra
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2022, 13 (SUPPL 1) : 699 - 709
  • [8] Sentiment Analysis Meets Explainable Artificial Intelligence: A Survey on Explainable Sentiment Analysis
    Diwali, Arwa
    Saeedi, Kawther
    Dashtipour, Kia
    Gogate, Mandar
    Cambria, Erik
    Hussain, Amir
    IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2024, 15 (03) : 837 - 846
  • [9] A Hybrid Approach for Financial Sentiment Analysis Using Artificial Intelligence and Cuckoo Search
    Kansal, Vani
    Kumar, Rakesh
    2019 5TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING & COMMUNICATION SYSTEMS (ICACCS), 2019, : 523 - 528
  • [10] Sarcasmometer using Sentiment Analysis and Topic Modeling
    Bhan, Namrata
    D'silva, Mitchell
    2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND CONTROL (ICAC3), 2017,