Standard
Sentiment analysis under temporal shift. / Lukes, Jan; Søgaard, Anders.
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, 2018. p. 65–71.
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Lukes, J & Søgaard, A 2018, Sentiment analysis under temporal shift. in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, pp. 65–71, 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium, 31/10/2018.
APA
Lukes, J., & Søgaard, A. (2018). Sentiment analysis under temporal shift. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 65–71). Association for Computational Linguistics.
Vancouver
Lukes J, Søgaard A. Sentiment analysis under temporal shift. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics. 2018. p. 65–71
Author
Lukes, Jan ; Søgaard, Anders. / Sentiment analysis under temporal shift. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, 2018. pp. 65–71
Bibtex
@inproceedings{ac0f745e21b144e39685eeb219c9f15f,
title = "Sentiment analysis under temporal shift",
abstract = "Sentiment analysis models often rely ontraining data that is several years old. Inthis paper, we show that lexical featureschange polarity over time, leading to degradingperformance. This effect is particularlystrong in sparse models relyingonly on highly predictive features. Usingpredictive feature selection, we are able tosignificantly improve the accuracy of suchmodels over time.",
author = "Jan Lukes and Anders S{\o}gaard",
year = "2018",
language = "English",
pages = "65–71",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
publisher = "Association for Computational Linguistics",
note = "null ; Conference date: 31-10-2018 Through 31-10-2018",
}
RIS
TY - GEN
T1 - Sentiment analysis under temporal shift
AU - Lukes, Jan
AU - Søgaard, Anders
PY - 2018
Y1 - 2018
N2 - Sentiment analysis models often rely ontraining data that is several years old. Inthis paper, we show that lexical featureschange polarity over time, leading to degradingperformance. This effect is particularlystrong in sparse models relyingonly on highly predictive features. Usingpredictive feature selection, we are able tosignificantly improve the accuracy of suchmodels over time.
AB - Sentiment analysis models often rely ontraining data that is several years old. Inthis paper, we show that lexical featureschange polarity over time, leading to degradingperformance. This effect is particularlystrong in sparse models relyingonly on highly predictive features. Usingpredictive feature selection, we are able tosignificantly improve the accuracy of suchmodels over time.
M3 - Article in proceedings
SP - 65
EP - 71
BT - Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
PB - Association for Computational Linguistics
Y2 - 31 October 2018 through 31 October 2018
ER -
