Sentiment analysis under temporal shift

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
PublisherAssociation for Computational Linguistics
Publication date2018
Pages65–71
Publication statusPublished - 2018
Event9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis - Brussels, Belgium
Duration: 31 Oct 2018 → 31 Oct 2018

Workshop

Workshop9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
LandBelgium
ByBrussels
Periode31/10/2018 → 31/10/2018

ID: 214758960