Multimodal sentiment analysis using hierarchical fusion with context modeling

Poria, 2017, Context-dependent sentiment analysis in user-generated videos, 873

Cambria, 2016, Affective computing and sentiment analysis, IEEE Intell. Syst., 31, 102, 10.1109/MIS.2016.31

Poria, 2017, A review of affective computing: From unimodal analysis to multimodal fusion, Inf. Fusion, 37, 98, 10.1016/j.inffus.2017.02.003

Hazarika, 2018, Conversational memory network for emotion recognition in dyadic dialogue videos, 2122

Chaturvedi, 2018, Distinguishing between facts and opinions for sentiment analysis: Survey and challenges, Inf. Fusion, 44, 65, 10.1016/j.inffus.2017.12.006

Lo, 2017, Multilingual sentiment analysis: From formal to informal and scarce resource languages, Artif. Intell. Rev., 48, 499, 10.1007/s10462-016-9508-4

Peng, 2018, Learning multi-grained aspect target sequence for chinese sentiment analysis, Knowl. Based Syst., 148, 167, 10.1016/j.knosys.2018.02.034

Bandhakavi, 2017, Lexicon generation for emotion analysis of text, IEEE Intell. Syst., 32, 102, 10.1109/MIS.2017.22

Dragoni, 2018, OntoSenticNet: A commonsense ontology for sentiment analysis, IEEE Intell. Syst., 33, 77, 10.1109/MIS.2018.033001419

Cambria, 2018, SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings, 1795

Oneto, 2016, Statistical learning theory and ELM for big social data analysis, IEEE Comput. Intell. Mag., 11, 45, 10.1109/MCI.2016.2572540

Hussain, 2018, Semi-supervised learning for big social data analysis, Neurocomputing, 275, 1662, 10.1016/j.neucom.2017.10.010

Li, 2017, Learning word representations for sentiment analysis, Cognit. Comput., 9, 843, 10.1007/s12559-017-9492-2

Young, 2018, Recent trends in deep learning based natural language processing, IEEE Comput. Intell. Mag., 13, 55, 10.1109/MCI.2018.2840738

Li, 2018, A generative model for category text generation, Inf. Sci., 450, 301, 10.1016/j.ins.2018.03.050

Cambria, 2017, Sentiment analysis is a big suitcase, IEEE Intell. Syst., 32, 74, 10.1109/MIS.2017.4531228

Xia, 2015, Word polarity disambiguation using bayesian model and opinion-level features, Cognit. Comput., 7, 369, 10.1007/s12559-014-9298-4

Chaturvedi, 2018, Bayesian network based extreme learning machine for subjectivity detection, J. Franklin Inst., 355, 1780, 10.1016/j.jfranklin.2017.06.007

Majumder, 2017, Deep learning-based document modeling for personality detection from text, IEEE Intell. Syst., 32, 74, 10.1109/MIS.2017.23

Satapathy, 2017, Phonetic-based microtext normalization for twitter sentiment analysis, 407

Rajagopal, 2013, A graph-based approach to commonsense concept extraction and semantic similarity detection, 565

Zhong, 2017, Time expression analysis and recognition using syntactic token types and general heuristic rules, 420

Ma, 2018, Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM, 5876

Xing, 2018, Natural language based financial forecasting: A survey, Artif. Intell. Rev., 50, 49, 10.1007/s10462-017-9588-9

Ebrahimi, 2017, Challenges of sentiment analysis for dynamic events, IEEE Intell. Syst., 32, 70, 10.1109/MIS.2017.3711649

Cambria, 2010, Sentic computing for patient centered application, 1279

Valdivia, 2017, Sentiment analysis in tripadvisor, IEEE Intell. Syst., 32, 72, 10.1109/MIS.2017.3121555

Mihalcea, 2016, What men say, what women hear: Finding gender-specific meaning shades, IEEE Intell. Syst., 31, 62, 10.1109/MIS.2016.71

Cavallari, 2017, Learning community embedding with community detection and node embedding on graphs, 377

Xu, 2017, Adaptive two-stage feature selection for sentiment classification, 1238

Zadeh, 2018, Multi-attention recurrent network for human communication comprehension, 5642

Young, 2018, Augmenting end-to-end dialogue systems with commonsense knowledge, 4970

De Silva, 1997, Facial emotion recognition using multi-modal information, vol. 1, 397

Chen, 1998, Multimodal human emotion/expression recognition, 366

Kessous, 2010, Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis, J. Multimodal User Interfaces, 3, 33, 10.1007/s12193-009-0025-5

Schuller, 2011, Recognizing affect from linguistic information in 3d continuous space, IEEE Trans. Affect. Comput., 2, 192, 10.1109/T-AFFC.2011.17

Wollmer, 2013, Youtube movie reviews: Sentiment analysis in an audio-visual context, IEEE Intell. Syst., 28, 46, 10.1109/MIS.2013.34

Rozgic, 2012, Ensemble of SVM trees for multimodal emotion recognition, 1

Metallinou, 2008, Audio-visual emotion recognition using gaussian mixture models for face and voice, 250

Eyben, 2010, On-line emotion recognition in a 3-d activation-valence-time continuum using acoustic and linguistic cues, J. Multimodal User Interfaces, 3, 7, 10.1007/s12193-009-0032-6

Wu, 2011, Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels, IEEE Trans. Affect. Comput., 2, 10, 10.1109/T-AFFC.2010.16

Poria, 2016, Convolutional MKL based multimodal emotion recognition and sentiment analysis, 439

Zadeh, 2017, Tensor fusion network for multimodal sentiment analysis, 1114

Poria, 2017, Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis, Neurocomputing, 261, 217, 10.1016/j.neucom.2016.09.117

Poria, 2015, Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis, 2539

Karpathy, 2014, Large-scale video classification with convolutional neural networks, 1725

T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, arXiv:1301.3781 (2013).

Teh, 2001, Rate-coded restricted boltzmann machines for face recognition, vol. 13, 908

Eyben, 2010, Opensmile: the munich versatile and fast open-source audio feature extractor, 1459

Ji, 2013, 3D convolutional neural networks for human action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 35, 221, 10.1109/TPAMI.2012.59

Majumder, 2017

Kingma, 2014, Adam: A method for stochastic optimization, CoRR, abs/1412.6980

Zadeh, 2016, Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages, IEEE Intell. Syst., 31, 82, 10.1109/MIS.2016.94

Busso, 2008, Iemocap: Interactive emotional dyadic motion capture database, Lang. Resour. Eval., 42, 335, 10.1007/s10579-008-9076-6

Pérez-Rosas, 2013, Utterance-level multimodal sentiment analysis, 973