| Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM Y Ma, H Peng, E Cambria Proceedings of AAAI, 5876-5883, 2018 | 665 | 2018 |
| Knowing what, how and why: A near complete solution for aspect-based sentiment analysis H Peng, L Xu, L Bing, F Huang, W Lu, L Si Proceedings of the AAAI conference on artificial intelligence 34 (05), 8600-8607, 2020 | 341 | 2020 |
| Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis Y Ma, H Peng, T Khan, E Cambria, A Hussain Cognitive Computation 10, 639-650, 2018 | 299 | 2018 |
| Sentiment and Sarcasm Classification with Multitask Learning N Majumder, S Poria, H Peng, N Chhaya, E Cambria, A Gelbukh IEEE Intelligent Systems 34 (3), 2019 | 228 | 2019 |
| Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis S Poria, H Peng, A Hussain, N Howard, E Cambria Neurocomputing 261, 217-230, 2017 | 223 | 2017 |
| A review of sentiment analysis research in Chinese language H Peng, E Cambria, A Hussain Cognitive Computation 9, 423-435, 2017 | 188 | 2017 |
| Towards scalable and reliable capsule networks for challenging NLP applications W Zhao, H Peng, S Eger, E Cambria, M Yang arXiv preprint arXiv:1906.02829, 2019 | 165 | 2019 |
| Learning multi-grained aspect target sequence for Chinese sentiment analysis H Peng, Y Ma, Y Li, E Cambria Knowledge-Based Systems 148, 167-176, 2018 | 156 | 2018 |
| Disentangled variational auto-encoder for semi-supervised learning Y Li, Q Pan, S Wang, H Peng, T Yang, E Cambria Information Sciences 482, 73-85, 2019 | 94 | 2019 |
| Learning binary codes with neural collaborative filtering for efficient recommendation systems Y Li, S Wang, Q Pan, H Peng, T Yang, E Cambria Knowledge-Based Systems 172, 64-75, 2019 | 92 | 2019 |
| BabelSenticNet: A commonsense reasoning framework for multilingual sentiment analysis D Vilares, H Peng, R Satapathy, E Cambria 2018 IEEE symposium series on computational intelligence (SSCI), 1292-1298, 2018 | 80 | 2018 |
| Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning H Peng, Y Ma, S Poria, Y Li, E Cambria Information Fusion 70, 88-99, 2021 | 50 | 2021 |
| Radical-based hierarchical embeddings for chinese sentiment analysis at sentence level H Peng, E Cambria, X Zou Proceedings of FLAIRS, 347-352, 2017 | 45 | 2017 |
| End-to-end latent-variable task-oriented dialogue system with exact log-likelihood optimization H Xu, H Peng, H Xie, E Cambria, L Zhou, W Zheng World Wide Web 23 (3), 1989-2002, 2020 | 42 | 2020 |
| Cross-lingual aspect-based sentiment analysis with aspect term code-switching W Zhang, R He, H Peng, L Bing, W Lam Proceedings of the 2021 conference on empirical methods in natural language …, 2021 | 34 | 2021 |
| CSenticNet: a concept-level resource for sentiment analysis in Chinese language H Peng, E Cambria Proceedings of CICLing, 90-104, 2017 | 14 | 2017 |
| Fusing Phonetic Features and Chinese Character Representation for Sentiment Analysis H Peng, S Poria, Y Li, E Cambria International Conference on Computational Linguistics and Intelligent Text …, 2019 | | 2019 |
| Linguistic-inspired Chinese sentiment analysis: from characters to radicals and phonetics H Peng | | 2019 |
Erik CambriaProfessor of Computing and Data Science, NTU Singapore在 ntu.edu.sg 的电子邮件经过验证
