The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A survey of sentiment analysis in social media
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Citations
Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181
McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.20 · Cited by 665
The eNTERFACE’05 Audio-Visual Emotion Database
Martin, O., Kotsia, I., Macq, B., & Pitas, I. (2006) · 22nd International Conference on Data Engineering Workshops (ICDEW'06) · doi:10.1109/icdew.2006.145 · Cited by 615
Fusing audio, visual and textual clues for sentiment analysis from multimodal content
Poria, S., Cambria, E., Howard, N., Huang, G.-B., & Hussain, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.01.095 · Cited by 534 · cited 2×
“Besides, there are novel fusion types such as hybrid multimodal fusion combining both of feature-level fusion and decision-level fusion methods [97, 99], model-level fusion using the correlation between data under different modalities [9, 142], rule-based fusion [28], classification-based fusion using a range of class…”
BackgroundMethodLSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.03.001 · Cited by 288 · cited 1×
“…fusion and decision-level fusion methods [97, 99], model-level fusion using the correlation between data under different modalities [9, 142], rule-based fusion [28], classification-based fusion using a range of classification algorithms to classify the…”
MethodAn unobtrusive behavioral model of "gross national happiness"
Kramer, A. D. I. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753369 · Cited by 247
Towards an intelligent framework for multimodal affective data analysis
Poria, S., Cambria, E., Hussain, A., & Huang, G.-B. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.10.005 · Cited by 236
Cohen, I., Sebe, N., Gozman, F. G., Cirelo, M. C., & Huang, T. S. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211408 · Cited by 155
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
Transformer 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
Self-citationA systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 615
Self-citationImage-Text Multimodal Emotion Classification via Multi-View Attentional Network
Yang, X., Feng, S., Wang, D., & Zhang, Y. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3035277 · Cited by 267
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3021467 · Cited by 257
Self-citation
Metadata
- Authors
- Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M.
- Venue
- Knowledge and Information Systems
- Published
- 2018-07-04
- Volume
- 60
- Issue
- 2
- Pages
- 617-663
- ISSN
- 0219-1377 (ISSN-L)
- 0219-3116
- OpenAlex
- W2810665353
- Cited by
- 577
- References
- 9
- FWCI
- 17.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems. https://doi.org/10.1007/s10115-018-1236-4
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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.
