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. Affective computing and sentiment analysis. IEEE Intelligent Systems 31(2), pp. 102-107 (2016)

• 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)

• L Oneto, F Bisio, E Cambria, D Anguita. Statistical learning theory and ELM for big social data analysis. IEEE Computational Intelligence Magazine 11(3), pp. 45-55 (2016)

• 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, Vancouver (2017)

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

• 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, Lisbon (2015)

• S Poria, H Peng, A Hussain, N Howard, E Cambria. Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. Neurocomputing, in press (2017)

• S Poria, E Cambria, A Hussain, GB Huang. Towards an intelligent framework for multimodal affective data analysis. Neural Networks 63, pp. 104-116 (2015)


• SL Lo, E Cambria, R Chiong, D Cornforth. Multilingual sentiment analysis: From formal to informal and scarce resource languages. Artificial Intelligence Review, in press (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, Vancouver (2016)

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

• H Peng, E Cambria, A Hussain. A review of sentiment analysis research in Chinese language. Cognitive Computation, in press (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, R Bajpai, B Schuller. SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives. In: COLING, pp. 2666-2677, Osaka (2016)

• E Cambria, J Fu, F Bisio, S Poria. AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis. In: AAAI, pp. 508-514, Austin (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, Toronto (2012)

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

• E Cambria, B White. Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine 9(2), pp. 48-57 (2014)

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

• X Zhong, A Sun, E Cambria. Time expression analysis and recognition using syntactic token types and general heuristic rules. In: ACL, Vancouver (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, in press (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, Osaka (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)

• 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)

• N Howard, E Cambria. Intention awareness: Improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences 3(9) (2013)

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

• P Chandra, E Cambria, A Pradeep. Enriching social communication through semantics and sentics. In: IJCNLP, pp. 68-72, Chiang Mai (2011)