Encyclopedia of Big Data

Living Edition
| Editors: Laurie A. Schintler, Connie L. McNeely

Sentic Computing

  • Erik Cambria
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_513-1
With the recent development of deep learning, research in artificial intelligence (AI) has gained new vigor and prominence. Machine learning, however, suffers from three big issues, namely:
  1. 1.

    Dependency issue: it requires (a lot of) training data and it is domain-dependent.

     
  2. 2.

    Consistency issue: different training and/or tweaking lead to different results.

     
  3. 3.

    Transparency issue: the reasoning process is uninterpretable (black-box algorithms).

     

Sentic computing (Cambria and Hussain 2015) addresses these issues in the context of natural language processing (NLP) by coupling machine learning with linguistics and commonsense reasoning. In particular, we apply an ensemble of commonsense-driven linguistic patterns and statistical NLP: the former are triggered when prior knowledge is available, the latter is used as backup plan when both semantics and sentence structure are unknown. Machine learning, in fact, is only useful to make a good guessbecause it only encodes correlation and...

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Further Readings

  1. Cambria, E., & Hussain, A. (2015). Sentic computing: A common-sense-based framework for concept-level sentiment analysis. Cham: Springer.CrossRefGoogle Scholar
  2. Cambria, E., Chandra, P., Sharma, A., & Hussain, A. (2010). Do not feel the trolls. In ISWC. ShanghaiGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. Cambria, E., Olsher, D., & Kwok, K. (2012c). Sentic activation: A two-level affective common sense reasoning framework. In AAAI (pp. 186–192). Toronto.Google Scholar
  6. 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.Google Scholar
  7. Cambria, E., Rajagopal, D., Kwok, K., & Sepulveda, J. (2015b). GECKA: Game engine for commonsense knowledge acquisition. In FLAIRS (pp. 282–287).Google Scholar
  8. Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017a). A practical guide to sentiment analysis. Cham: Springer.CrossRefGoogle Scholar
  9. Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017b). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6), 74–80.CrossRefGoogle Scholar
  10. 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.Google Scholar
  11. 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.Google Scholar
  12. 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.CrossRefGoogle Scholar
  13. Ma, Y., Cambria, E., & Gao, S. (2016). Label embedding for zero-shot fine-grained named entity typing. In COLING (pp. 171–180). Osaka.Google Scholar
  14. 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.Google Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.Google Scholar
  18. 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.
  19. 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.Google Scholar
  20. 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.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore