ORCID
- Palomino, Marco: 0000-0001-7850-416X
Abstract
Online trends have established themselves as a new method of information propagation that is reshaping journalism in the digital age. We argue that sentiment analysis—the classification of human emotion expressed in text—can enhance existing algorithms for trend discovery. By highlighting topics that are polarised, sentiment analysis can offer insight into the influence of users who are involved in a trend, and how other users adopt such a trend. As a case study, we have investigated a highly topical subject: Brexit, the withdrawal of the United Kingdom from the European Union. We retrieved an experimental corpus of publicly available tweets referring to Brexit and used them to test a proposed algorithm to identify trends. We validate the efficiency of the algorithm and gauge the sentiment expressed on the captured trends to confirm that highly polarised data ensures the emergence of trends.
Publication Date
2019-09-01
Publication Title
2231-5403
Volume
9
Issue
12
ISSN
2231-5403
Embargo Period
2019-12-03
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Palomino, M., & Murali, A. (2019) '#Brexit vs. #StopBrexit: What is Trendier? An NLP Analysis', 2231-5403, 9(12). Retrieved from https://pearl.plymouth.ac.uk/secam-research/920
