The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis
Abstract
Deep learning has unlocked new paths towards the emulation of the peculiarly-human capability of learning from examples. While this kind of bottom-up learning works well for tasks such as image classification or object detection, it is not as effective when it comes to natural language processing. Communication is much more than learning a sequence of letters and words: it requires a basic understanding of the world and social norms, cultural awareness, commonsense knowledge, etc.; all things that we mostly learn in a top-down manner. In this work, we integrate top-down and bottom-up learning via an ensemble of symbolic and subsymbolic AI tools, which we apply to the interesting problem of polarity detection from text. In particular, we integrate logical reasoning within deep learning architectures to build a new version of SenticNet, a commonsense knowledge base for sentiment analysis.
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Lexicon-based methods for sentiment analysis
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301
Sentiment strength detection in short informal text
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449
Learning Explanatory Rules from Noisy Data
Evans, R., & Grefenstette, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5714 · Cited by 426
Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
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-citationTransformer models for text-based emotion detection: a review of BERT-based approaches
Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 659
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
Metadata
- Authors
- Cambria, E., Li, Y., Xing, F. Z., Poria, S., & Kwok, K.
- Venue
- Proceedings of the 29th ACM International Conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2020-10-19
- Pages
- 105-114
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W3094173182
- Cited by
- 437
- References
- 5
- FWCI
- 34.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cambria, E., Li, Y., Xing, F. Z., Poria, S., & Kwok, K. (2020). SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. Proceedings of the 29th ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3340531.3412003
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