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.
Dependency issue: it requires (a lot of) training data and it is domain-dependent.
- 2.
Consistency issue: different training and/or tweaking lead to different results.
- 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 its...
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Further Readings
Cambria, E., & Hussain, A. (2015). Sentic computing: A common-sense-based framework for concept-level sentiment analysis. Cham: Springer.
Cambria, E., Chandra, P., Sharma, A., & Hussain, A. (2010). Do not feel the trolls. In ISWC. Shanghai
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.
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.
Cambria, E., Olsher, D., & Kwok, K. (2012c). Sentic activation: A two-level affective common sense reasoning framework. In AAAI (pp. 186–192). Toronto.
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.
Cambria, E., Rajagopal, D., Kwok, K., & Sepulveda, J. (2015b). GECKA: Game engine for commonsense knowledge acquisition. In FLAIRS (pp. 282–287).
Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017a). A practical guide to sentiment analysis. Cham: Springer.
Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017b). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32(6), 74–80.
Cambria, E., Li, Y., Xing, Z., Poria, S., & Kwok, K. (2020). SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In CIKM. Ireland.
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.
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.
Ma, Y., Cambria, E., & Gao, S. (2016). Label embedding for zero-shot fine-grained named entity typing. In COLING (pp. 171–180). Osaka.
Ma, Y., Peng, H., & Cambria, E. (2018). Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In AAAIÂ (pp. 5876-5883). New Orleans.
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.
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.
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.
Susanto, Y., Livingstone, A., Ng, B.C., & Cambria, E. (2020) The Hourglass model revisited. IEEE Intelligent Systems 35(5).
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.
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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Cambria, E. (2020). Sentic Computing. In: Schintler, L.A., McNeely, C.L. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_513-2
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DOI: https://doi.org/10.1007/978-3-319-32001-4_513-2
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Sentic Computing- Published:
- 04 September 2020
DOI: https://doi.org/10.1007/978-3-319-32001-4_513-2
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Sentic Computing- Published:
- 30 April 2018
DOI: https://doi.org/10.1007/978-3-319-32001-4_513-1