Publications


JOURNAL ARTICLES
• 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)

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

• 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)

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

• 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)

• N Tran, E Cambria. Ensemble application of ELM and GPU for real-time multimodal sentiment analysis. Memetic Computing, in press (2017)

• 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, N Howard, GB Huang, A Hussain. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, pp. 50-59 (2016)

• 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)

• I Chaturvedi, YS Ong, I Tsang, R Welsch, E Cambria. Learning word dependencies in text by means of a deep recurrent belief network. Knowledge-Based Systems 108, pp. 144–154 (2016)

• N Tran, E Cambria, A Hussain. Towards GPU-based common-sense reasoning: Using fast subgraph matching. Cognitive Computation 8(6), pp. 1074-1086 (2016)

• R Xia, T Wang, JF Yu, F Xu, Y Qi, E Cambria. Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis. Information Processing and Management 52, pp. 36-45 (2016)

• 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)

• K Dashtipour, S Poria, A Hussain, E Cambria, A Hawalah, A Gelbukh, Q Zhou. Multilingual sentiment analysis: State of the art and independent comparison of techniques. Cognitive Computation 8(4), pp. 757–771 (2016)

• S Poria, E Cambria, A Gelbukh, F Bisio, A Hussain. Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Computational Intelligence Magazine 10(4), pp. 26-36 (2015)

• E Cambria, P Gastaldo, F Bisio, R Zunino. An ELM-based model for affective analogical reasoning. Neurocomputing 149, pp. 443-455 (2015)

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

• E Principi, S Squartini, E Cambria, F Piazza. Acoustic template-matching for automatic emergency state detection: An ELM based algorithm. Neurocomputing 149, pp. 426-434 (2015)

• G Tang, YQ Xia, E Cambria, P Jin. Inducing word senses for cross-lingual document clustering. International Journal of Pattern Recognition and Artificial Intelligence 29(2) (2015)

• YQ Xia, E Cambria, A Hussain, H Zhao. Word polarity disambiguation using Bayesian model and opinion-level features. Cognitive Computation 7(3), pp. 369–380 (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)

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

• S Poria, A Gelbukh, E Cambria, A Hussain, GB Huang. EmoSenticSpace: A novel framework for affective common-sense reasoning. Knowledge-Based Systems 69, pp. 108-123 (2014)

• A Qazi, R Raj, M Tahir, E Cambria, K Syed. Enhancing business intelligence by means of suggestive reviews. The Scientific World Journal (2014)

• E Cambria, B Schuller, YQ Xia, C Havasi. New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems 28(2), pp. 15-21 (2013)

• E Cambria, GB Huang, et al. Extreme learning machines. IEEE Intelligent Systems 28(6), pp. 30-59 (2013)

• E Cambria, T Mazzocco, A Hussain. Application of multi-dimensional scaling and artificial neural networks for biologically inspired opinion mining. Biologically Inspired Cognitive Architectures 4, pp. 41-53 (2013)

• P Gastaldo, R Zunino, E Cambria, S Decherchi. Combining ELM with random projections. IEEE Intelligent Systems 28(6), pp. 46-48 (2013)

• S Decherchi, P Gastaldo, R Zunino, E Cambria, J Redi. Circular-ELM for the reduced-reference assessment of perceived image quality. Neurocomputing 102, pp. 78–89 (2013)

• QF Wang, E Cambria, CL Liu, A Hussain. Common sense knowledge for handwritten Chinese recognition. Cognitive Computation 5(2), pp. 234-242 (2013)

• R Xia, CQ Zong, XL Hu, E Cambria. Feature ensemble plus sample selection: Domain adaptation for sentiment classification. IEEE Intelligent Systems 28(3), pp. 10-18 (2013)

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

• N Howard, E Cambria. Development of a diplomatic, information, military, health, economic effects modeling system. International Journal of Privacy and Health Information Management 1(1), pp. 1-11 (2013)

• 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)

• M Grassi, E Cambria, A Hussain, F Piazza. Sentic web: A new paradigm for managing social media affective information. Cognitive Computation 3(3), pp. 480-489 (2011)

CONFERENCE PAPERS
• F Xing, E Cambria, X Zou. Predicting evolving chaotic time series with fuzzy neural networks. In: IJCNN, Anchorage (2017)

• GB Chen, DH Ye, E Cambria, JS Chen, ZC Xing. Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: IJCNN, Anchorage (2017)

• I Chaturvedi, S Cavallari, E Cambria, V Zheng. Learning word vectors in deep walk using convolution. In: FLAIRS, Marco Island (2017)

• H Peng, E Cambria, X Zou. Radical-based hierarchical embeddings for Chinese sentiment analysis at sentence level. In: FLAIRS, Marco Island (2017)

• C Sanli, A Mondal, E Cambria. Tracing linguistic relations in winning and losing sides of explicit opposing groups. In: FLAIRS, Marco Island (2017)

• A Mondal, E Cambria, D Das, S Bandyopadhyay. MediConceptNet: An affinity score based medical concept network. In: FLAIRS, Marco Island (2017)

• M Jaiswal, S Tabibu, E Cambria. Hang in there: Lexical and visual analysis to identify posts warranting empathetic responses. In: FLAIRS, Marco Island (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)

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

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

• S Poria, I Chaturvedi, E Cambria, F Bisio. Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. In: IJCNN, pp. 4465-4473, Vancouver (2016)

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

• N Tran, E Cambria. GpSense: A GPU-friendly method for common-sense subgraph matching in massively parallel architectures. In: CICLing, Konya (2016)

• F Xing, E Cambria, WB Huang, Y Xu. Weakly supervised semantic segmentation with superpixel embedding. In: ICIP, pp. 1269-1273, Phoenix (2016)

• E Cambria, T Nguyen, B Cheng, K Kwok, J Sepulveda. GECKA3D: A 3D game engine for commonsense knowledge acquisition. In: AAAI FLAIRS, pp. 299-303, Key Largo (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)

• 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)

• F Bisio, P Gastaldo, R Zunino, E Cambria. A learning scheme based on similarity functions for affective common-sense reasoning. In: IJCNN, pp. 2476-2481, Killarney (2015)

• E Cambria, G Melfi. Semantic outlier detection for affective common-sense reasoning and concept-level sentiment analysis. In: AAAI FLAIRS, pp. 276-281, Hollywood (2015)

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

• R Xia, CQ Zong, XL Hu, E Cambria. Feature ensemble plus sample selection: Domain adaptation for sentiment classification (extended abstract). In: IJCAI, pp. 4229-4233, Buenos Aires (2015)

• C Antuvan, F Bisio, E Cambria, L Masia. Muscle synergies for reliable classification of arm motions using myoelectric interface. In: IEEE EMBS, Milan (2015)

• C Antuvan, F Bisio, E Cambria, L Masia. Discrete classification of upper limb motions using myoelectric interface. In: IEEE ICORR, pp. 451-456, Singapore (2015)

• E Cambria, D Olsher, D Rajagopal. SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In: AAAI, pp. 1515-1521, Quebec City (2014)

• S Poria, E Cambria, LW Ku, C Gui, A Gelbukh. A rule-based approach to aspect extraction from product reviews. In: COLING, Dublin (2014)

• E Cambria, N Howard. Common and common-sense knowledge integration for concept-level sentiment analysis. In: AAAI FLAIRS, pp. 170-173, Pensacola Beach (2014)

• E Lunadei, C Valdivia, E Cambria. Collective Copyright: Enabling the natural evolution of content creation in the Web Era. In: WWW, pp. 1103-1108, Seoul (2014)

• D Rajagopal, E Cambria, D Olsher, K Kwok. A graph-based approach to commonsense concept extraction and semantic similarity detection. In: WWW, pp. 565-570, Rio De Janeiro (2013)

• E Cambria, N Howard, J Hsu, A Hussain. Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics. In: IEEE SSCI, pp. 108-117, Singapore (2013)

• D Rajagopal, D Olsher, E Cambria, K Kwok. Commonsense-based topic modeling. In: ACM KDD, Chicago (2013)

• F Bisio, E Cambria, P Gastaldo, C Peretti, R Zunino. Data intensive review mining for sentiment classification across heterogeneous domains. In: FOSINT-SI, Niagara Falls (2013)

• E Cambria. An introduction to concept-level sentiment analysis. In: MICAI, pp. 478-483, Mexico City (2013)

• E Cambria, I Barnes, E Brooks, C Eckl. WebSci@UHI: Teaching web science in a web science fashion. In: ICEL, pp. 505-509, Cape Town (2013)

• 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, C Havasi, A Hussain. SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: AAAI FLAIRS, pp. 202-207, Marco Island (2012)

• E Cambria, YQ Xia, A Hussain. Affective common sense knowledge acquisition for sentiment analysis. In: LREC, pp. 3580-3585, Istanbul (2012)

• E Cambria, D Olsher, K Kwok. Sentic panalogy: Swapping affective common sense reasoning strategies and foci. In: CogSci, pp. 174-179, Sapporo (2012)

• S Poria, A Gelbukh, E Cambria, D Das, S Bandyopadhyay. Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. In: IEEE ICDM, pp. 709-716, Brussels (2012)

• E Cambria, Y Song, H Wang, A Hussain. Isanette: A common and common sense knowledge base for opinion mining. In: IEEE ICDM, pp. 315-322, Vancouver (2011)

• E Cambria, A Hussain, C Eckl. Bridging the gap between structured and unstructured health-care data through semantics and sentics. In: ACM WebSci, Koblenz (2011)

• A Leoncini, F Sangiacomo, C Peretti, S Argentesi, R Zunino, E Cambria. Semantic models for style-based text clustering. In: IEEE ICSC, pp. 75-82, Palo Alto (2011)

• E Cambria, A Hussain, C Eckl. Taking refuge in your personal sentic corner. In: IJCNLP, pp. 35-43, Chiang Mai (2011)

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

• E Cambria, R Speer, C Havasi, A Hussain. SenticNet: A publicly available semantic resource for opinion mining. In: AAAI CSK, pp. 14-18, Arlington (2010)

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

• E Cambria, A Hussain, T Durrani, C Havasi, C Eckl, J Munro. Sentic computing for patient centered applications. In: IEEE ICSP, pp. 1279-1282, Beijing (2010)

• E Cambria, A Hussain, C Havasi, C Eckl, J Munro. Towards crowd validation of the UK national health service. In: ACM WebSci, Raleigh (2010)

• E Cambria, A Hussain, C Havasi, C Eckl. AffectiveSpace: Blending common sense and affective knowledge to perform emotive reasoning. In: CAEPIA, pp. 32-41, Seville (2009)

BOOKS
• E Cambria, D Das, S Bandyopadhyay. A Practical Guide to Sentiment Analysis. Springer (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)

• E Cambria, A Hussain. Sentic Computing: Techniques, Tools, Applications. Dordrecht, Netherlands: Springer, ISBN: 978-94-007-5069-2 (2012)

BOOK CHAPTERS
• F Bisio, L Oneto, E Cambria. Sentic computing for social network analysis. In: F. Pozzi, E Fersini, E Messina, B Liu (eds.) Sentiment Analysis in Social Networks, pp. 71-90, Elsevier (2016)

• F Bisio, C Meda, P Gastaldo, R Zunino, E Cambria. Sentiment-oriented information retrieval: Affective analysis of documents based on the SenticNet framework. In: W. Pedrycz and SM. Chen (eds.) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol. 639, pp. 175-197 (2016)

• E Cambria, S Poria, F Bisio, R Bajpai, I Chaturvedi. The CLSA Model: A novel framework for concept-level sentiment analysis. In: LNCS, vol. 9042, pp. 3-22, Springer (2015)

• P Chikersal, S Poria, E Cambria, A Gelbukh, ES Chng. Modelling public sentiment in Twitter: Using linguistic patterns to enhance supervised learning. In: LNCS, vol. 9042, pp. 49-65, Springer (2015)

• D Reforgiato, E Cambria. ESWC'14 challenge on concept-level sentiment analysis. In: Communications in Computer and Information Science, vol. 475, pp. 3-20, Springer (2014)

• E Cambria, D Rajagopal, D Olsher, D Das. Big social data analysis. In: R. Akerkar (ed.) Big Data Computing, ch. 13, pp. 401-414, Taylor & Francis (2013)

• E Cambria, M Grassi, S Poria, A Hussain. Sentic computing for social media analysis, representation, retrieval. In: N. Ramzan et al. (eds.) Social Media Retrieval, ch. 9, pp. 191-215, Springer (2013)

• E Cambria, A Livingstone, A Hussain. The hourglass of emotions. In: LNCS, vol. 7403, pp. 144-157, Springer (2012)

• A Hussain, E Cambria, T Mazzocco, M Grassi, QF Wang, T Durrani. Towards IMACA: Intelligent multimodal affective conversational agent. In: LNCS, vol. 7663, pp. 656–663, Springer (2012)

• P Chandra, E Cambria, A Hussain. Clustering social networks using interaction semantics and sentics. In: LNCS, vol. 7367, pp. 379-385, Springer (2012)

• I Hupont, E Cambria, E Cerezo, A Hussain, S Baldassarri. Sentic maxine: Multimodal affective fusion and emotional paths. In: LNCS, vol. 7368, pp. 555-565, Springer (2012)

• T Mazzocco, E Cambria, A Hussain, QF Wang. Sentic neural networks: A novel cognitive model for affective common sense reasoning. In: LNAI, vol. 7366, pp. 12-21, Springer (2012)

• E Cambria, A Hussain, T Durrani, JJ Zhang. Towards a Chinese common and common sense knowledge base for sentiment analysis. In: LNCS, vol. 7345, pp. 437-446, Springer (2012)

• E Cambria, T Mazzocco, A Hussain, T Durrani.Switching between different ways to think: Multiple approaches to affective common sense reasoning. In: LNCS, vol. 6800, pp. 56-69, Springer (2011)

• E Cambria, T Mazzocco, A Hussain, C Eckl. Sentic medoids: Organizing affective common sense knowledge in a multi-dimensional vector space. In: LNCS, vol. 6677, pp. 601-610, Springer (2011)

• E Cambria, I Hupont, A Hussain, E Cerezo, S Baldassarri. Sentic avatar: Multimodal affective conversational agent with common sense. In: LNCS, vol. 6456, pp. 81-95, Springer (2011)

• E Cambria, A Hussain, C Havasi, C Eckl. SenticSpace: Visualizing opinions and sentiments in a multi-dimensional vector space. In: LNAI, vol. 6279, pp. 385–393, Springer (2010)

• E Cambria, A Hussain, C Havasi, C Eckl. Sentic computing: Exploitation of common sense for the development of emotion-sensitive systems. In: LNCS, vol. 5967, pp. 148-156, Springer (2010)

• E Cambria, A Hussain, C Havasi, C Eckl. Common sense computing: From the society of mind to digital intuition and beyond. In: LNCS, vol. 5707, pp. 252-259, Springer (2009)