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Explaining Recurrent Neural Network Predictions in Sentiment Analysis
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Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.In the present work, we extend the usage of LRP to recurrent neural networks.We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs.We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.
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El–Sappagh, S., Alonso, J. M., Abuhmed, T., Ali, F., & Bugarín, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10415-5 · Cited by 58
Fiok, K., Karwowski, W., Gutiérrez-Franco, E., & Wilamowski, M. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115771 · Cited by 50
Chang-Brahim, I., Koppensteiner, L. J., Beltrame, L., Bodner, G., Saranti, A., Salzinger, J., Fanta‐Jende, P., Sulzbachner, C., Bruckmüller, F., Trognitz, F., et al. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1319938 · Cited by 58
DRAU: Dual Recurrent Attention Units for Visual Question Answering
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Self-citationEvaluating eXplainable artificial intelligence tools for hard disk drive predictive maintenance
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Towards robust explanations for deep neural networks
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Self-citationSAM: The Sensitivity of Attribution Methods to Hyperparameters
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Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance
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Interpretation of NLP models through input marginalization
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Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning
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A novel normalized recurrent neural network for fault diagnosis with noisy labels
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Interpreting Deep Learning Models for Knowledge Tracing
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Contextual Sentiment Neural Network for Document Sentiment Analysis
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Reverse-engineering Recurrent Neural Network solutions to a hierarchical inference task for micePreprint
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XFlag: Explainable Fake News Detection Model on Social Media
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On Guaranteed Optimal Robust Explanations for NLP Models
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Metadata
- Authors
- Arras, L., Montavon, G., Müller, K.-R., & Samek, W.
- Affiliations
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
- Technische Universität Berlin (DE)
- Freie Universität Berlin (DE)
- Venue
- Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Published
- 2017
- Pages
- 159-168
- OpenAlex
- W2639206954
- Cited by
- 361
- References
- 5
- Citations by year
- 2026: 6 · 2025: 22 · 2024: 42 · 2023: 49 · 2022: 47 · 2021: 63 · 2020: 58 · 2019: 42 · 2018: 26 · 2017: 6
- FWCI
- 16.46
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Institute for Information and Communications Technology Promotion (2017-0-00451)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- layer-wise relevance propagation, explainable AI, recurrent neural networks, sentiment analysis, LSTM, GRU, bidirectional LSTM
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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