Publications


Below is the list of our key publications organized under seven umbrellas:

EXPLAINABLE SENTIMENT ANALYSIS
PERSONALIZED SENTIMENT ANALYSIS
MULTIMODAL SENTIMENT ANALYSIS
MULTILINGUAL SENTIMENT ANALYSIS
MULTITASK SENTIMENT ANALYSIS
FINANCIAL SENTIMENT ANALYSIS
CONVERSATIONAL SENTIMENT ANALYSIS

sentic umbrellas

For the full list of our publications, please check Google Scholar




EXPLAINABLE SENTIMENT ANALYSIS
• E Cambria, X Zhang, R Mao, M Chen, K Kwok. SenticNet 8: Fusing emotion AI and commonsense AI for interpretable, trustworthy, and explainable affective computing. In: HCII (2024)

• E Cambria, R Mao, M Chen, Z Wang, SB Ho. Seven pillars for the future of artificial intelligence. IEEE Intelligent Systems 38(6), 62-69 (2023)

• E Cambria, L Malandri, F Mercorio, M Mezzanzanica, N Nobani. A survey on XAI and natural language explanations. Information Processing and Management 60, 103111 (2023)

• WJ Yeo, R Satapathy, SM Goh, E Cambria. How interpretable are reasoning explanations from prompting large language models? NAACL (2024)

• Y Susanto, A Livingstone, BC Ng, E Cambria. The Hourglass model revisited. IEEE Intelligent Systems 35(5), 96-102 (2020)

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

• B Liang, H Su, L Gui, E Cambria, R Xu. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowledge-Based Systems 235, 107643 (2022)

• A Diwali, K Saeedi, K Dashtipour, M Gogate, E Cambria, A Hussain. Sentiment analysis meets explainable artificial intelligence: A survey on sentiment analysis explainability. IEEE Transactions on Affective Computing 15 (2024)

• R Mao, Q Liu, K He, W Li, E Cambria. The biases of pre-trained language models: An empirical study on prompt-based sentiment analysis and emotion detection. IEEE Transactions on Affective Computing 14(3), 1743-1753 (2023)

• Z Yang, X Du, E Cambria, C Cardie. End-to-end case-based reasoning for commonsense knowledge base completion. In: EACL, 3509-3522 (2023)

• JF Cui, ZX Wang, SB Ho, E Cambria. Survey on sentiment analysis: Evolution of research methods and topics. Artificial Intelligence Review 56, 8469-8510 (2023)

sentic activation




PERSONALIZED SENTIMENT ANALYSIS
• L Zhu, W Li, R Mao, E Cambria. HIPPL: Hierarchical intent-inferring pointer network with pseudo labeling for consistent persona-driven dialogue generation. IEEE Computational Intelligence Magazine (2024)

• L Zhu, W Li, R Mao, V Pandelea, E Cambria. PAED: Zero-shot persona attribute extraction in dialogues. ACL, 9771-9787 (2023)

• Y Li, A Kazameini, Y Mehta, E Cambria. Multitask learning for emotion and personality traits detection. Neurocomputing 493, 340-350 (2022)

• AK Jayaraman, G Ananthakrishnan, TE Trueman, E Cambria. Text-based personality prediction using XLNet. Advances in Computers 132, 49-65 (2024)

• J Salminen, S Jung, H Almerekhi, E Cambria, B Jansen. How can natural language processing and generative AI address grand challenges of quantitative user personas?. International Conference on Human-Computer Interaction, 211-231 (2023)

• S Dhelim, N Aung, M Bouras, H Ning, E Cambria. A survey on personality-aware recommendation systems. Artificial Intelligence Review 55, 2409-2454 (2022)

• Y Mehta, N Majumder, A Gelbukh, E Cambria. Recent trends in deep learning based personality detection. Artificial Intelligence Review 53, 2313-2339 (2020)

• Y Mehta, S Fatehi, A Kazameini, C Stachl, E Cambria, S Eetemadi. Bottom-up and top-down: Predicting personality with psycholinguistic and language model features. In: ICDM, 1184-1189 (2020)

• A Kazemeini, SS Roy, RE Mercer, E Cambria. Interpretable representation learning for personality detection. Proceedings of ICDM Workshops, 158-165 (2021)

• A Kumar, T Trueman, E Cambria. Gender-based multi-aspect sentiment detection using multilabel learning. Information Sciences 606, 453-468 (2022)

personalized sentiment analysis




MULTIMODAL SENTIMENT ANALYSIS
• C Fan, J Lin, R Mao, E Cambria. Fusing pairwise modalities for emotion recognition in conversations. Information Fusion 106, 102306 (2024)

• K Zhang, YQ Li, JG Wang, E Cambria, XL Li. Real-time video emotion recognition based on reinforcement learning and domain knowledge. IEEE Trans on Circuits and Systems for Video Technology 32(3), 1034-1047 (2022)

• A Gandhi, K Adhvaryu, S Poria, E Cambria, A Hussain. Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Information Fusion 91, 424-444 (2023)

• T Yue, R Mao, H Wang, Z Hu, E Cambria. KnowleNet: Knowledge fusion network for multimodal sarcasm detection. Information Fusion 100, 101921 (2023)

• B Liang, L Gui, Y He, E Cambria, R Xu. Fusion and discrimination: A multimodal graph contrastive learning framework for multimodal sarcasm detection. IEEE Transactions on Affective Computing 15 (2024)

• L Stappen, A Baird, E Cambria, BW Schuller Sentiment analysis and topic recognition in video transcriptions. IEEE Intelligent Systems 36(2), 88-95 (2021)

• L Stappen, L Schumann, B Sertolli, A Baird, B Weigell, E Cambria MuSe-Toolbox: The multimodal sentiment analysis continuous annotation fusion and discrete class transformation toolbox. International Multimedia Conference, 75-82 (2021)

• L Stappen, A Baird, G Rizos, P Tzirakis, X Du, F Hafner, L Schumann, A Mallol-Ragolta, B Schuller, I Lefter, E Cambria, I Kompatsiaris. MuSe 2020 Challenge and Workshop: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media. In: ACM Multimedia, 35-44 (2020)

• E Cambria, D Hazarika, S Poria, A Hussain, RBV Subramaanyam. Benchmarking multimodal sentiment analysis. In: CICLing, 166-179 (2017)

• I Chaturvedi, R Satapathy, S Cavallari, E Cambria. Fuzzy commonsense reasoning for multimodal sentiment analysis. Pattern Recognition Letters 125, 264-270 (2019)

multimodal sentiment analysis




MULTILINGUAL SENTIMENT ANALYSIS
• T Yue, X Shi, R Mao, Z Hu, E Cambria. SarcNet: A multilingual multimodal sarcasm detection dataset. In: LREC-COLING (2024)

• D Vilares, H Peng, R Satapathy, E Cambria. BabelSenticNet: A commonsense reasoning framework for multilingual sentiment analysis. In: IEEE SSCI, 1292-1298 (2018)

• P Le-Hong, E Cambria. A semantics-aware approach for multilingual natural language inference. Language Resources and Evaluation 57, 611-639 (2023)

• P Le-Hong, E Cambria. Integrating graph embedding and neural models for improving transition-based dependency parsing. Neural Computing and Applications (2023)

• Z Wang, X Zhang, J Cui, SB Ho, E Cambria. A review of Chinese sentiment analysis: Subjects, methods, and trends. Artificial Intelligence Review (2024)

• M Bounhas, B Elayeb, A Chouigui, A Hussain, E Cambria. Arabic text classification based on analogical proportions. Expert Systems (2024)

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

• ALS Mohammad, MM Hammad, A Sa’ad, ALT Saja, E Cambria. Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis. Knowledge-Based Systems 261, 107540 (2023)

• H Peng, Y Ma, S Poria, Yang Li, E Cambria. Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning. Information Fusion 70, 88-99 (2021)

• H Peng, Y Ma, Y Li, E Cambria. Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowledge-Based Systems 148, 167-176 (2018)

multilingual sentiment analysis




MULTITASK SENTIMENT ANALYSIS
• M Firdaus, A Ekbal, E Cambria. Multitask learning for multilingual intent detection and slot filling in dialogue systems. Information Fusion 91, 299-315 (2023)

• R Satapathy, E Cambria. Polarity and subjectivity detection with multitask learning and BERT embedding. Future Internet 14(7), 191 (2022)

• X Zhang, R Mao, K He, E Cambria. Neurosymbolic sentiment analysis with dynamic word sense disambiguation. In: EMNLP, 8772-8783 (2023)

• D Jiang, R Wei, H Liu, J Wen, G Tu, L Zheng, E Cambria. A Multitask learning framework for multimodal sentiment analysis. In: ICDM Workshops, 151-157 (2021)

• N Majumder, S Poria, H Peng, N Chhaya, E Cambria, A Gelbukh. Sentiment and sarcasm classification with multitask learning. IEEE Intelligent Systems 34(3), 38-43 (2019)

• R Liu, G Chen, R Mao, E Cambria. A multi-task learning model for gold-two-mention co-reference resolution. IJCNN (2023)

• K He, R Mao, T Gong, C Li, E Cambria. Meta-based self-training and re-weighting for aspect-based sentiment analysis. IEEE Transactions on Affective Computing 14(3), 1731-1742 (2023)

• M Ge, R Mao, E Cambria. Explainable metaphor identification inspired by conceptual metaphor theory. In: AAAI, 10681-10689 (2022)

• A Valdivia, MV Luzón, E Cambria, F Herrera. Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion 44, 126-135 (2018)

• K He, R Mao, Y Huang, T Gong, C Li, E Cambria. Template-free prompting for few-shot named entity recognition via semantic-enhanced contrastive learning. IEEE Transactions on Neural Networks and Learning Systems (2024)

sarcasm detection




FINANCIAL SENTIMENT ANALYSIS
• WJ Yeo, W Van Der Heever, R Mao, E Cambria, R Satapathy, G Mengaldo. A comprehensive review on financial explainable AI. arXiv preprint arXiv:2309.11960 (2024)

• K Du, F Xing, R Mao, E Cambria. Financial sentiment analysis: Techniques and applications. ACM Computing Surveys (2024)

• Y Ma, R Mao, Q Lin, P Wu, E Cambria. Quantitative stock portfolio optimization by multi-task learning risk and return. Information Fusion 104, 102165 (2024)

• R Mao, K Du, Y Ma, L Zhu, E Cambria. Discovering the cognition behind language: Financial metaphor analysis with MetaPro. In: ICDM (2023)

• K Ong, W van der Heever, R Satapathy, G Mengaldo, E Cambria. FinXABSA: Explainable finance through aspect-based sentiment analysis. In: ICDM Workshops, 773-782 (2023)

• K Du, F Xing, R Mao, E Cambria. FinSenticNet: A concept-level lexicon for financial sentiment analysis. In: IEEE SSCI, 109-114 (2023)

• K Du, F Xing, E Cambria. Incorporating multiple knowledge sources for targeted aspect-based financial sentiment analysis. ACM Transactions on Management Information Systems 14(3), 23 (2023)

• Z Wang, Z Hu, F Li, SB Ho, E Cambria. Learning-based stock trending prediction by incorporating technical indicators and social media sentiment. Cognitive Computation 15(3), 1092-1102 (2023)

• Y Ma, R Mao, Q Lin, P Wu, E Cambria. Multi-source aggregated classification for stock price movement prediction. Information Fusion 91, 515-528 (2023)

• F Xing, L Malandri, Y Zhang, E Cambria. Financial sentiment analysis: An investigation into common mistakes and silver bullets. In: COLING, 978-987 (2020)

NLFF




CONVERSATIONAL SENTIMENT ANALYSIS

• M Amin, E Cambria, B Schuller. Will affective computing emerge from foundation models and General AI? A first evaluation on ChatGPT. IEEE Intelligent Systems 38(2), 15-23 (2023)

• W Li, L Zhu, W Shao, Z Yang, E Cambria. Task-aware self-supervised framework for dialogue discourse parsing. In: EMNLP, 14162-14173 (2023)

• W Li, L Zhu, R Mao, E Cambria. SKIER: A symbolic knowledge integrated model for conversational emotion recognition. In: AAAI, 13121-13129 (2023)

• D Jiang, H Liu, G Tu, R Wei, E Cambria. Self-supervised utterance order prediction for emotion recognition in conversations. Neurocomputing 577, 127370 (2024)

• W Li, Y Li, V Pandelea, M Ge, L Zhu, E Cambria. ECPEC: Emotion-cause pair extraction in conversations. IEEE Transactions on Affective Computing 14(3), 1754-1765 (2023)

• D Jiang, R Wei, J Wen, G Tu, E Cambria. AutoML-Emo: Automatic knowledge selection using congruent effect for emotion identification in conversations. IEEE Transactions on Affective Computing 14(3), 1845-1856 (2023)

• D Varshney, A Ekbal, E Cambria. Emotion-and-knowledge grounded response generation in an open-domain dialogue setting. Knowledge-Based Systems 284, 111173 (2024)

• J Wen, D Jiang, G Tu, C Liu, E Cambria. Dynamic interactive multiview memory network for emotion recognition in conversation. Information Fusion 91, 123-133 (2023)

• W Li, W Shao, SX Ji, E Cambria. BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis. Neurocomputing 467, 73-82 (2022)

• N Mishra, M Ramanathan, R Satapathy, E Cambria, N Thalmann. Can a humanoid robot be part of the organizational workforce? A user study leveraging sentiment analysis. In: Ro-Man (2019)


human-robot interaction