The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Affective Computing and Sentiment Analysis
Abstract
Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.
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Cites
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
The Cognitive Structure of Emotions
Ortony, A., Clore, G. L., & Collins, A. (1988) · doi:10.1017/cbo9780511571299 · Cited by 6,208
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893
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
Self-citationGunes, H., & Schuller, B. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.016 · Cited by 360
Affective multimodal human-computer interaction
Pantic, M., Sebe, N., Cohn, J. F., & Huang, T. (2005) · Proceedings of the 13th annual ACM international conference on Multimedia · doi:10.1145/1101149.1101299 · Cited by 234
Cited by
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-citationSentiment 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
Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages
Zadeh, A., Zellers, R., Pincus, E., & Morency, L.-P. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.94 · Cited by 852
Machine learning algorithms for social media analysis: A survey
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100395 · Cited by 276
Akhtar, M. S., Ekbal, A., & Cambria, E. (2020) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2019.2954667 · Cited by 246
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Metadata
- Authors
- Cambria, E.
- Venue
- IEEE Intelligent Systems
- Published
- 2016-03
- Volume
- 31
- Issue
- 2
- Pages
- 102-107
- ISSN
- 1541-1672 (ISSN-L)
- OpenAlex
- W2306941105
- Cited by
- 1,204
- References
- 7
- FWCI
- 109.82
- Type
- journal article
- Language
- en
- Open access
- closed
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