TY - GEN
T1 - Topic Analysis in News About AI
T2 - 13th World Conference on Information Systems and Technologies, WorldCIST 2025
AU - Galán-Mena, Jorge
AU - López-Nores, Martín
AU - Galán, Josué
AU - Pulla-Sánchez, Daniel
AU - Guerrero-Vásquez, Luis F.
AU - Salgado-Guerrero, Juan P.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Topic analysis within broad and evolving fields poses great challenges when attempting to be addressed by traditional methods. In response, topic modeling seeks to automate the identification and analysis of underlying themes within several collections of documents in order to synthesize and facilitate the interpretation of their content. The present study applied multiple iterations of the Latent Dirichlet Allocation (LDA) model and the Best-K mechanism in the identification of topics within a volume of 250 news items labeled with “Artificial Intelligence” within a mainstream web portal. Additionally, a large language model (LLM) was implemented to improve interpretation and description for the labeling of found topics. As a result, 9 key topics were identified ranging from the main trends and challenges in AI.
AB - Topic analysis within broad and evolving fields poses great challenges when attempting to be addressed by traditional methods. In response, topic modeling seeks to automate the identification and analysis of underlying themes within several collections of documents in order to synthesize and facilitate the interpretation of their content. The present study applied multiple iterations of the Latent Dirichlet Allocation (LDA) model and the Best-K mechanism in the identification of topics within a volume of 250 news items labeled with “Artificial Intelligence” within a mainstream web portal. Additionally, a large language model (LLM) was implemented to improve interpretation and description for the labeling of found topics. As a result, 9 key topics were identified ranging from the main trends and challenges in AI.
KW - Automatic topic labeling
KW - Large Language Models (LLM)
KW - Latent Dirichlet Allocation
KW - News media analysis
KW - Topic modeling
UR - https://www.scopus.com/pages/publications/105023103351
U2 - 10.1007/978-3-032-01130-5_28
DO - 10.1007/978-3-032-01130-5_28
M3 - Contribución a la conferencia
AN - SCOPUS:105023103351
SN - 9783032011299
T3 - Lecture Notes in Networks and Systems
SP - 355
EP - 366
BT - Emerging Trends in Information Systems and Technologies - WorldCIST 2025 Volume 2
A2 - Rocha, Alvaro
A2 - Adeli, Hojjat
A2 - Poniszewska-Maranda, Aneta
A2 - Moreira, Fernando
A2 - Bianchi, Isaias
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 15 April 2025 through 17 April 2025
ER -