The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Attention-based LSTM for Aspect-level Sentiment Classification
Wang, Y., Huang, M., zhu, X., & Zhao, L. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1058 · Cited by 2,450
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Self-citationContext-Dependent Sentiment Analysis in User-Generated Videos
Poria, S., Cambria, E., Hazarika, D., Majumder, N., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1081 · Cited by 939
Self-citation
Cited by
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.005 · Cited by 745
Self-citationYang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020) · IEEE Access · doi:10.1109/access.2020.2969854 · Cited by 573
Abbass, H. A. (2019) · Cognitive Computation · doi:10.1007/s12559-018-9619-0 · Cited by 200
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
Metadata
- Authors
- Ma, Y., Peng, H., Khan, T., Cambria, E., & Hussain, A.
- Venue
- Cognitive Computation
- Published
- 2018-03-14
- Volume
- 10
- Issue
- 4
- Pages
- 639-650
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W2790309729
- Cited by
- 309
- References
- 6
- FWCI
- 21.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ma, Y., Peng, H., Khan, T., Cambria, E., & Hussain, A. (2018). Sentic LSTM: a Hybrid Network for Targeted Aspect-Based Sentiment Analysis. Cognitive Computation. https://doi.org/10.1007/s12559-018-9549-x
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
