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
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)
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)
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)
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)
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)
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)
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)