The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Open accessAbstract
Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding. In this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges of both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge. We augment the long short-term memory (LSTM) network with a hierarchical attention mechanism consisting of a target-level attention and a sentence-level attention. Commonsense knowledge of sentiment-related concepts is incorporated into the end-to-end training of a deep neural network for sentiment classification. In order to tightly integrate the commonsense knowledge into the recurrent encoder, we propose an extension of LSTM, termed Sentic LSTM. We conduct experiments on two publicly released datasets, which show that the combination of the proposed attention architecture and Sentic LSTM can outperform state-of-the-art methods in targeted aspect sentiment tasks.
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SemEval-2014 Task 4: Aspect Based Sentiment Analysis
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Self-citationDesign challenges and misconceptions in named entity recognition
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SemEval-2016 Task 5: Aspect Based Sentiment Analysis
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SemEval-2015 Task 12: Aspect Based Sentiment Analysis
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Self-citationRecent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Self-citationAn Attentive Survey of Attention Models
Chaudhari, S., Mithal, V., Polatkan, G., & Ramanath, R. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3465055 · Cited by 689
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230975 · Cited by 567
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
Li, X., Bing, L., Zhang, W., & Lam, W. (2019) · Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) · doi:10.18653/v1/d19-5505 · Cited by 348
Metadata
- Authors
- Ma, Y., Peng, H., & Cambria, E.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-26
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2788810909
- Cited by
- 592
- References
- 36
- FWCI
- 38.28
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Ma, Y., Peng, H., & Cambria, E. (2018). Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v32i1.12048
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