Sentiment analysis under temporal shift

Jan Lukes, Anders Søgaard


Abstract
Sentiment analysis models often rely on training data that is several years old. In this paper, we show that lexical features change polarity over time, leading to degrading performance. This effect is particularly strong in sparse models relying only on highly predictive features. Using predictive feature selection, we are able to significantly improve the accuracy of such models over time.
Anthology ID:
W18-6210
Volume:
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
65–71
Language:
URL:
https://aclanthology.org/W18-6210
DOI:
10.18653/v1/W18-6210
Bibkey:
Cite (ACL):
Jan Lukes and Anders Søgaard. 2018. Sentiment analysis under temporal shift. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 65–71, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Sentiment analysis under temporal shift (Lukes & Søgaard, WASSA 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/W18-6210.pdf