Public attitudes toward higher education using sentiment analysis and topic modeling
Yükleniyor...
Dosyalar
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Nature
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This study examines higher education through data-mining methodologies, aiming to uncover key themes and sentiments in global discourse. Utilizing sentiment analysis and topic modeling, the research analyzes 157,943 tweets from 84,423 unique users over a five-month period (January to May 2023). This period was selected, coinciding with the rise of artificial intelligence (AI) tools, particularly ChatGPT. The study investigates the discussions, emotional tones, and dominant topics shaping the global narrative of higher education within X (Twitter) data. Key findings include the geographical distribution of tweets and the most frequent positive and negative perceptions. It also addresses critical issues such as affordability, accessibility, and funding in higher education. Furthermore, the data shows public reactions to AI in higher education are initially negative, while higher education tweets are primarily characterized by positivity and optimism. The higher education tweets are mainly posted on the weekend, with decreased activity during weekdays. This research provides insights into the evolving higher education landscape amid rapid technological advancements.
Açıklama
Anahtar Kelimeler
Higher Education, Text Mining, Topic Modeling, X/Twitter, Sentiment Analysis, Artificial Intelligence, ChatGPT
Kaynak
Discover Artificial Intelligence
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
4
Sayı
1
Künye
Göçen, A., Ibrahim, M. M. ve Khan, A. I. (2024). Public attitudes toward higher education using sentiment analysis and topic modeling. Discover Artificial Intelligence, 4(1), 1-19. https://www.doi.org/10.1007/s44163-024-00195-4
