The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Aspect Level Sentiment Classification with Deep Memory Network
Open accessAbstract
We introduce a deep memory network for aspect level sentiment classification.Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect.Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory.Experiments on laptop and restaurant datasets demonstrate that our approach performs comparable to state-of-art feature based SVM system, and substantially better than LSTM and attention-based LSTM architectures.On both datasets we show that multiple computational layers could improve the performance.Moreover, our approach is also fast.The deep memory network with 9 layers is 15 times faster than LSTM with a CPU implementation.
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Relational Graph Attention Network for Aspect-based Sentiment Analysis
Wang, K., Shen, W., Yang, Y., Quan, X., & Wang, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.295 · Cited by 665
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
Do, H. H., Prasad, P., Maag, A., & Alsadoon, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.003 · Cited by 614
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
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Metadata
- Authors
- Tang, D., Qin, B., & Liu, T.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 214-224
- OpenAlex
- W2963168371
- Cited by
- 1,041
- References
- 40
- FWCI
- 49.19
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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