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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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An Introduction to Detection of Hate Speech and Offensive Language in Slovak
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A Survey of Explainable Artificial Intelligence Approaches for Sentiment Analysis
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GF-LRP: A Method for Explaining Predictions Made by Variational Graph Auto-Encoders
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Wide & Deep Learning in Job Recommendation: An Empirical Study
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Deep Learning Approaches for Textual Sentiment Analysis
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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.
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