Below is the list of our most recent/important publications organized under six umbrellas:


For the full list of publications, please check Google Scholar


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

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

• S Poria, E Cambria, A Gelbukh. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems 108, pp. 42-49 (2016)

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

• E Cambria, D Das, S Bandyopadhyay, A Feraco. A Practical Guide to Sentiment Analysis. Cham, Switzerland: Springer, ISBN: 978-3-319-55394-8 (2017)

• E Cambria, A Hussain. Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Cham, Switzerland: Springer, ISBN: 978-3-319-23654-4 (2015)

• S Poria, E Cambria, R Bajpai, A Hussain. A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion 37, pp. 98-125 (2017)

• S Poria, E Cambria, D Hazarika, N Mazumder, A Zadeh, L Morency. Context-dependent sentiment analysis in user-generated videos. In: ACL, pp. 873-883 (2017)

• S Poria, I Chaturvedi, E Cambria, A Hussain. Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: ICDM, pp. 439-448 (2016)

• S Poria, E Cambria, D Hazarika, N Mazumder, A Zadeh, LP Morency. Multi-level multiple attentions for context-aware multimodal sentiment analysis. In: ICDM (2017)

• A Zadeh, M Chen, S Poria, E Cambria, LP Morency. Tensor fusion network for multimodal sentiment analysis. In: EMNLP, pp. 1114-1125 (2017)

• S Poria, E Cambria, A Gelbukh. Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: EMNLP, pp. 2539–2544 (2015)


• SL Lo, E Cambria, R Chiong, D Cornforth. Multilingual sentiment analysis: From formal to informal and scarce resource languages. Artificial Intelligence Review 48(4), pp. 499-527 (2017)

• SL Lo, E Cambria, R Chiong, D Cornforth. A multilingual semi-supervised approach in deriving Singlish sentic patterns for polarity detection. Knowledge-Based Systems 105, pp. 236–247 (2016)

• I Chaturvedi, E Cambria, D Vilares. Lyapunov filtering of objectivity for Spanish sentiment model. In: IJCNN, pp. 4474-4481 (2016)

• H Peng, E Cambria. CSenticNet: A concept-level resource for sentiment analysis in Chinese language. In: CICLing (2017)

• H Peng, E Cambria, A Hussain. A review of sentiment analysis research in Chinese language. Cognitive Computation 9(4), pp. 423-435 (2017)

• H Peng, E Cambria, X Zou. Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level. In: FLAIRS, pp. 347-352 (2017)


• E Cambria, S Poria, D Hazarika, K Kwok. SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings. In: AAAI (2018)

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

• L Oneto, F Bisio, E Cambria, D Anguita. Semi-supervised learning for affective common-sense reasoning. Cognitive Computation 9(1), pp. 18–42 (2017)

• E Cambria, YQ Song, H Wang, N Howard. Semantic multi-dimensional scaling for open-domain sentiment analysis. IEEE Intelligent Systems 29(2), pp. 44-51 (2014)

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

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

• T Young, D Hazarika, S Poria, E Cambria. Recent trends in deep learning based natural language processing. arXiv preprint arXiv:1708.02709 (2017)

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

• X Zhong, A Sun, E Cambria. Time expression analysis and recognition using syntactic token types and general heuristic rules. In: ACL, pp. 420-429 (2017)

• I Chaturvedi, E Ragusa, P Gastaldo, R Zunino, E Cambria. Bayesian network based extreme learning machine for subjectivity detection. Journal of The Franklin Institute, doi:10.1016/j.jfranklin.2017.06.007 (2017)

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

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

• T Young, E Cambria, I Chaturvedi, S Biswas, H Zhou, M Huang. Augmenting end-to-end dialogue systems with commonsense knowledge. In: AAAI (2018)

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

• Y Ma, H Peng, E Cambria. Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM. In: AAAI (2018)

• E Cambria, T Benson, C Eckl, A Hussain. 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), pp. 10533–10543 (2012)

• E Cambria, A Hussain. Sentic album: Content-, concept-, context-based online personal photo management system. Cognitive Computation 4(4), pp. 477-496 (2012)

• E Cambria, M Grassi, A Hussain, C Havasi. Sentic computing for social media marketing. Multimedia Tools and Applications 59(2), pp. 557-577 (2012)