Comparative Sentiment Analysis on News Coverage of AI Risks and Regulation using Rule-based and Transformer-based Models
Abstract
need for adequate regulation. This study aims to analyze the dynamics of public opinion through media coverage of AI risks and
regulation. Data were obtained from five major international media outlets (Reuters, Bloomberg, The Guardian, CNBC, and The
New York Times) between 2022 and 2025. The analysis process was carried out in several stages: news article extraction, text
cleaning, sentiment classification, and trend and distribution visualization. Two approaches were used for sentiment analysis: a
rule-based lexical model (VADER) and a contextual transformer model (Multilingual BERT from nlptown). Classification results
show that VADER tends to assign neutral labels, while BERT is more sensitive to positive or negative nuances. Correlations between
models indicate general trends, but differences emerge during specific periods—particularly during periods of intense coverage of AI
policy formulation or ethical incidents. Temporal visualizations show spikes in negative sentiment during the enactment of AI
regulations in several countries. This study concludes that the multi-model approach is capable of capturing a broader spectrum of
sentiment. Limitations include limited media coverage, potential data bias, and the model's limited ability to understand
domain-specific contexts. Recommendations for further study include expanding data sources, using models specifically trained in
the AI policy domain, and integrating with entity analysis to uncover dominant actors in public discourse.
Full Text:
PDFReferences
A. Reuel and T. A. Undheim, Generative AI Needs Adaptive
Governance, arXiv preprint, 2024.
A. Batool, D. Zowghi, and M. Bano, Responsible AI
Governance: A Systematic Literature Review, arXiv
preprint, 2023.
C. J. Hutto and E. Gilbert, “VADER: A Parsimonious
Rule-Based Model for Sentiment Analysis of Social Media
Text,” in Proc. Int. AAAI Conf. on Web and Social Media,
vol. 8, no. 1, pp. 216–225, 2014.
D. K. Nugroho, “Sentiment Analysis for Predicting the 2020
US Presidential Election Using VADER,” in Proc. 11th Int.
Conf. on Cloud Computing, Data Science & Engineering
(Confluence), Noida, India, 2021, pp. 136–141.
P. Saha, S. Mahato, and A. Das, “VADER vs. BERT: A
Comparative Performance Analysis for Sentiment on
Coronavirus Outbreak,” in Recent Advances in
Computational Intelligence, Singapore: Springer, 2023, pp.
–418.
X. Lu, L. Li, Y. Xu, B. Z. Zhang, and L. Hemphill,
Landscape of Generative AI in Global News: Topics,
Sentiments, and Spatiotemporal Analysis, arXiv preprint,
N. Balabanova, A. Bashir, et al., Media and Responsible AI
Governance: A Game-Theoretic and LLM Analysis, arXiv
preprint, 2025.
F. N. Ribeiro, M. Araújo, P. Gonçalves, M. A. Gonçalves,
and F. Benevenuto, SentiBench: A Benchmark Comparison
of State-of-the-Practice Sentiment Analysis Methods, EPJ
Data Science, vol. 5, no. 1, p. 23, 2016.
C. J. Hutto and E. Gilbert, VADER: A Parsimonious
Rule-Based Model for Sentiment Analysis of Social Media
Text, in Proc. Int. AAAI Conf. on Web and Social Media, vol.
, no. 1, pp. 216–225, 2014.
L. Zhang, S. Wang, and B. Liu, Deep Learning for Sentiment
Analysis: A Survey, Wiley Interdisciplinary Reviews: Data
Mining and Knowledge Discovery, vol. 8, no. 4, e1253,
J. Devlin, M.-W. Chang, K. Lee, dan K. Toutanova, "BERT:
Pre-training of Deep Bidirectional Transformers for
Language Understanding," Proceedings of NAACL-HLT,
W. Yin, K. Kann, M. Yu, dan H. Schütze, "Comparative
Analysis of BERT-based Models for Sentiment
Classification," Proceedings of ACL, 2021.
T. Hoang, T. H. Nguyen, dan M. T. Nguyen, "Benchmarking
Sentiment Analysis Models in Financial and Political
News," Journal of Computational Social Science, vol. 5, pp.
–20, 2022.
A. Jobin, M. Ienca, and E. Vayena, "The global landscape of
AI ethics guidelines," Nature Machine Intelligence, vol. 1,
no. 9, pp. 389–399, 2019.
B. Binns, "Fairness in machine learning: Lessons from
political philosophy," Proceedings of the 2020 ACM
Conference on Fairness, Accountability, and Transparency,
pp. 149–159, 2020.
DOI: https://doi.org/10.29040/ijcis.v6i3.251
Article Metrics
Abstract view : 7 timesPDF - 3 times
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 4.0 International License
