Sentiment analysis under temporal shift
By Jan Lukes, Anders Søgaard. 2018
41 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Graham Neubig
- Luke S. Zettlemoyer
- Kyunghyun Cho
- Jianfeng Gao
- Noah A. Smith
- Yejin Choi
- Percy Liang
In Natural Language Processing
- Graham Neubig
- Mohit Bansal
- Kyunghyun Cho
- Noah A. Smith
- Sebastian Ruder
- Yejin Choi
- Richard Socher
- Jianfeng Gao
Citations in the corpus
- Time-Aware Language Models as Temporal Knowledge Bases (2021, background): “Several studies have focused on degradation of models on test data from a different time period than their training data (Huang and Paul, 2018, 2019; Jaidka et al., 2018; Lukes and Søgaard, 2018; Florio et al., 2020).”
- Mind the Gap: Assessing Temporal Generalization in Neural Language Models (2021, methodology): “…training on text from the past and evaluating on future text) for NLP tasks like machine translation (Levenberg et al., 2010), sentiment analysis (Lukes and Søgaard, 2018), named entity recognition (Fromreide et al., 2014; Rijhwani and Preotiuc-Pietro, 2020), and others (Dunn et al., 2017;…”
- Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021, background): “, 2018), sentiment analysis (Lukes and Søgaard, 2018) and hate speech”
- Temporal Effects on Pre-trained Models for Language Processing Tasks (2021, methodology): “The results are consistent with prior work that uses non-neural models (Fromreide et al., 2014; Lukes and Søgaard, 2018; Huang and Paul, 2018) or evaluates on a single test set (Lukes and Søgaard, 2018; Rijhwani and Preotiuc-Pietro, 2020; Søgaard et al., 2021).”