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Sentic Computing

Encyclopedia of Big Data

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  • Cambria, E., & Hussain, A. (2015). Sentic computing: A common-sense-based framework for concept-level sentiment analysis. Cham: Springer.

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  • Cambria, E., Chandra, P., Sharma, A., & Hussain, A. (2010). Do not feel the trolls. In ISWC. Shanghai

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  • Cambria, E., Benson, T., Eckl, C., & Hussain, A. (2012a). Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications, 39(12), 10533–10543.

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  • Cambria, E., Livingstone, A., & Hussain, A. (2012b). The hourglass of emotions. In A. Esposito, A. Vinciarelli, R. Hoffmann, & V. Muller (Eds.), Cognitive behavioral systems, Lecture notes in computer science (Vol. 7403, pp. 144–157). Berlin/Heidelberg: Springer.

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  • Cambria, E., Olsher, D., & Kwok, K. (2012c). Sentic activation: A two-level affective common sense reasoning framework. In AAAI (pp. 186–192). Toronto.

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  • Cambria, E., Fu, J., Bisio, F., & Poria, S. (2015a). AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis. In AAAI (pp. 508–514). Austin.

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  • Cambria, E., Rajagopal, D., Kwok, K., & Sepulveda, J. (2015b). GECKA: Game engine for commonsense knowledge acquisition. In FLAIRS (pp. 282–287).

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  • Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017a). A practical guide to sentiment analysis. Cham: Springer.

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  • Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017b). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6), 74–80.

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  • Cambria, E., Poria, S., Hazarika, D., & Kwok, K. (2018). SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In AAAI (pp. 1795-1802). New Orleans.

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  • Cavallari, S., Zheng, V., Cai, H., Chang, K., & Cambria, E. (2017). Learning community embedding with community detection and node embedding on graphs. In CIKM (pp. 377–386). Singapore.

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  • Chaturvedi, I., Ragusa, E., Gastaldo, P., Zunino, R., & Cambria, E. (2018). Bayesian network based extreme learning machine for subjectivity detection. Journal of The Franklin Institute, 355(4), 1780–1797.

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  • Ma, Y., Cambria, E., & Gao, S. (2016). Label embedding for zero-shot fine-grained named entity typing. In COLING (pp. 171–180). Osaka.

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  • Ma, Y., Peng, H., & Cambria, E. (2018). Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In AAAI (pp. 5876-5883). New Orleans.

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  • Majumder, N., Poria, S., Gelbukh, A., & Cambria, E. (2017). Deep learning-based document modeling for personality detection from text. IEEE Intelligent Systems, 32(2), 74–79.

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  • Poria, S., Cambria, E., Winterstein, G., & Huang, G.-B. (2014). Sentic patterns: Dependency-based rules for concept-level sentiment analysis. Knowledge-Based Systems, 69, 45–63.

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  • Poria, S., Cambria, E., Hazarika, D., & Vij, P. (2016). A deeper look into sarcastic tweets using deep convolutional neural networks. In COLING (pp. 1601–1612). Osaka.

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  • Xing, F., Cambria, E., & Welsch, R. (2018). Natural language based financial forecasting: A survey. Artificial Intelligence Review. https://doi.org/10.1007/s10462-017-9588-9.

  • Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., & Huang, M. (2018). Augmenting end-to-end dialog systems with commonsense knowledge. In AAAI (pp. 4970-4977). New Orleans.

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  • Zadeh, A., Liang, P. P., Poria, S., Vij, P., Cambria, E., & Morency, L.-P. (2018). Multi-attention recurrent network for human communication comprehension. In AAAI (pp. 5642-5649). New Orleans.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

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  1. Erik Cambria
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Correspondence to Erik Cambria .

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Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, Virginia, USA

    Connie L. McNeely

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Cambria, E. (2018). Sentic Computing. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_513-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_513-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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Chapter history

  1. Latest

    Sentic Computing
    Published:
    04 September 2020

    DOI: https://doi.org/10.1007/978-3-319-32001-4_513-2

  2. Original

    Sentic Computing
    Published:
    30 April 2018

    DOI: https://doi.org/10.1007/978-3-319-32001-4_513-1

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