Dr Yunfei Long
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Email
yl20051@essex.ac.uk -
Location
4B.521, Colchester Campus
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Academic support hours
Thursday 16:00-18:00
Profile
Biography
My research is focused on natural language understanding, currently, I have three research threads: 1: Applying linguistic knowledge bases to fundamental NLP models, including LLMs. 2: NLP/LLM applications in healthcare, and digital wellbeing. 3: Neuro-Cognitive NLP and its application in affective analysis/misinformation detection based on text and multimodality data.
Qualifications
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PhD The Hong Kong Polytechnic University, (2019)
Research and professional activities
Research interests
Developing novel Natural Language Processing and Explainable AI for modelling healthcare data
This research aims to develop a series of improvement works for NLP and XAI in clinical data and other health-related data that will provide: 1. Adapt Responsible Research & Innovation (RRI) methods to start the interdisciplinary clinical data science and NLP works in Essex. 2. Prototype a person-centered supervised model training process to refine definitions and expose and explore tacit and latent knowledge in psychotherapy assessment. 3. Identify the key factors contributing to performance and trust in the model pipeline (data, processing, deployment) by examining domain expert requirements for the qualities of an engaging, interactive feedback interface and eliciting broader concerns about its acceptability. 4. Assess whether patient and practitioner knowledge base in developing a digital mental health decision support tool increases performance and trust in it.
Leveraging Lexical Semantics in Affective Analysis (and other NLP applications)
This study aims to investigate novel models to incorporate features of lexical items and ontologies in English from external resources into neural NLP frameworks. This allows the models to take the combined effects of bi-directional feeling between the professional knowledge and the text to infer human affective understanding and to the wider Natural language understanding.
Large Language Models for Games
Autonomous Communication, Learning, and Interaction in Game NPCs: Immersive Gameplay forge deeper connections with realistic NPCs, transforming storytelling and user engagement within the digital gaming landscape.
Misinformation and affective analysis
In 2022, misinformation ranked as the second highest online threat in the UK, with 22% encountering it within a month (Ofcom, 2022). This issue, intricately tied to emotion, spans platforms like social media and fields such as politics. People heavily influenced by emotions are more prone to fake news, and when guided by emotions, their susceptibility increases, a stark contrast to logic-driven individuals. The emotional pull plays a pivotal role in misinformation vulnerability, potentially disrupting social media communities. Moreover, misinformation breeds distrust even towards credible news. Further complicating matters, figurative language in misinformation can mislead, especially when cultural idioms are taken out of context. It's vital to distinguish between misinformation and satire, both of which can employ similar rhetorical tools, risking context loss in the digital realm. Counterstrategies must understand the emotional, cognitive, and cultural underpinnings that shape misinformation beliefs (Carmen Sanchez, 2021). Initial fake news detection relied on linguistic cues. Several research endeavours have adopted diverse detection techniques, from focusing on sentiment indicators in content to leveraging advanced neural networks for tweet analysis (Kashyap Popat, 2016). Contemporary Natural Language Processing (NLP) leans towards holistic approaches. Yet, few tackle misinformation from an emotional angle. Our project fills this gap, focusing on emotion, metaphor, and irony in misinformation detection. This effort holds potential for significant policy implications, highlighting the importance of emotion-centric analysis in battling misinformation.
Teaching and supervision
Current teaching responsibilities
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Team Project Challenge (CE201)
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Natural Language Engineering (CE314)
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Natural Language Engineering (CE887)
Current supervision
Previous supervision
Degree subject: Computer Science
Degree type: Master of Science (by Dissertation)
Awarded date: 11/10/2024
Publications
Publications (1)
Yang, H., Hadjiantoni, S., Long, Y., Petraityte, R. and Lausen, B., (2023). Automatic Detection of Industry Sectors in Legal Articles Using Machine Learning Approaches
Journal articles (40)
Zhao, Q., Xia, Y., Long, Y., Xu, G. and Wang, J., (2025). Leveraging Sensory Knowledge into Text-to-Text Transfer Transformer for Enhanced Emotion Analysis. Information Processing and Management. 62 (1), 103876-103876
Lu, Q., Sun, X., Gao, Z., Long, Y., Feng, J. and Zhang, H., (2024). Coordinated-joint Translation Fusion Framework with Sentiment-interactive Graph Convolutional Networks for Multimodal Sentiment Analysis. Information Processing and Management. 61 (1), 103538-103538
Lu, Q., Long, Y., Sun, X., Feng, J. and Zhang, H., (2024). Fact-sentiment Incongruity Combination Network for Multimodal Sarcasm Detection. Information Fusion. 104 (104), 102203-102203
Zhao, Q., Long, Y., Jiang, X., Wang, Z., Huang, C-R. and Zhou, G., (2024). Linguistic Synesthesia Detection: Leveraging Culturally Enriched Linguistic Features. Natural Language Processing (previously Natural Language Engineering), 1-23
Huang, G., Li, Y., Jameel, S., Long, Y. and Papanastasiou, GP., (2024). From Explainable to lnterpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?. Computational and Structural Biotechnology Journal. 24, 362-373
Xia, T., Sun, X., Yang, Y., Long, Y. and Sutcliffe, R., (2024). A dual relation-encoder network for aspect sentiment triplet extraction. Neurocomputing. 597, 128064-128064
Lei, H., Tang, C. and Long, Y., (2024). Study on the impact of digital economy on industrial collaborative agglomeration: Evidence from manufacturing and productive service industries. PLOS ONE. 19 (8), e0308361-e0308361
Lu, Q., Sun, X., Long, Y., Zhao, X., Wang, Z., Feng, J. and Wang, X., (2024). Multimodal Dual Perception Fusion Framework for Multimodal Affective Analysis. Information Fusion. 115, 102747-102747
Lu, Q., Sun, X., Long, Y., Gao, Z., Feng, J. and Sun, T., (2023). Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges. IEEE Transactions on Neural Networks and Learning Systems. 35 (11), 15092-15112
Fang, H., Xu, G., Long, Y., Guan, Y., Yang, X., Chen, Z. and Long, Y., (2023). A System Review on Bootstrapping Information Extraction. Multimedia Tools and Applications. 83 (13), 38329-38353
Ito-Jaeger, S., Perez Vallejos, E., Curran, T., Spors, V., Long, Y., Liguori, A., Warwick, M., Wilson, M. and Crawford, P., (2022). Digital video interventions and mental health literacy among young people: a scoping review.. Journal of Mental Health. 31 (6), 873-883
Fang, H., Chen, C., Long, Y., Xu, G. and Xiao, Y., (2022). DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph. Mathematics. 10 (9), 1402-1402
Fang, H., Xu, G., Long, Y. and Tang, W., (2022). An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews. Applied Sciences. 12 (21), 10881-10881
Malins, S., Figueredo, G., Jilani, T., Long, Y., Andrews, J., Rawsthorne, M., Manolescu, C., Clos, J., Higton, F., Waldram, D., Hunt, D., Perez Vallejos, E. and Moghaddam, N., (2022). Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach.. JMIR Medical Informatics. 10 (11), e38168-e38168
Andrews, JA., Rawsthorne, M., Manolescu, C., Burton McFaul, M., French, B., Rye, E., McNaughton, R., Baliousis, M., Smith, S., Biswas, S., Baker, E., Repper, D., Long, Y., Jilani, T., Clos, J., Higton, F., Moghaddam, N. and Malins, S., (2022). Involving psychological therapy stakeholders in responsible research to develop an automated feedback tool: Learnings from the ExTRAPPOLATE project. Journal of Responsible Technology. 11, 100044-100044
Long, Y., Xiang, R., Lu, Q., Huang, C-R. and Li, M., (2021). Improving attention model based on cognition grounded data for sentiment analysis. IEEE Transactions on Affective Computing. 12 (4), 900-912
Lin, Z., Long, Y., Du, J. and Xu, R., (2021). A Multi-modal Sentiment Recognition Method Based on Multi-task Learning. Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis. 57 (1), 7-15
Xiang, R., Chersoni, E., Lu, Q., Huang, C., Li, W. and Long, Y., (2021). Lexical data augmentation for sentiment analysis. Journal of the Association for Information Science and Technology. 72 (11), 1432-1447
Jin, G., Zhou, J., Qu, W., Long, Y. and Gu, Y., (2021). Exploiting Rich Event Representation to Improve Event Causality Recognition. Intelligent Automation and Soft Computing. 29 (3), 161-173
Shi, H., Qu, W., Wei, T., Zhou, J., Long, Y., Gu, Y. and Li, B., (2021). Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases. Computers, Materials and Continua. 69 (3), 4113-4127
Wu, T., Zhou, J., Qu, W., Gu, Y., Li, B., Zhong, H. and Long, Y., (2021). Improving AMR parsing by exploiting the dependency parsing as an auxiliary task. Multimedia Tools and Applications. 80 (20), 30827-30838
Chen, I-H., Long, Y., Lu, Q. and Huang, C-R., (2021). Orthographic features for emotion classification in Chinese in informal short texts. Language Resources and Evaluation. 55 (2), 329-352
Ong, ZX., Dowthwaite, L., Perez Vallejos, E., Rawsthorne, M. and Long, Y., (2021). Measuring Online Wellbeing: A Scoping Review of Subjective Wellbeing Measures.. Frontiers in Psychology. 12, 616637-
Shen, J., Ma, MD., Xiang, R., Lu, Q., Vallejos, EP., Xu, G., Huang, C-R. and Long, Y., (2020). Dual memory network model for sentiment analysis of review text. Knowledge-Based Systems. 188, 105004-105004
Bergin, AD., Vallejos, EP., Davies, EB., Daley, D., Ford, T., Harold, G., Hetrick, S., Kidner, M., Long, Y., Merry, S., Morriss, R., Sayal, K., Sonuga-Barke, E., Robinson, J., Torous, J. and Hollis, C., (2020). Preventive digital mental health interventions for children and young people: a review of the design and reporting of research. npj Digital Medicine. 3 (1), 133-
Wei, T., Qu, W., Zhou, J., Long, Y., Gu, Y. and Xia, Z., (2020). Improving Chinese Word Representation with Conceptual Semantics. Computers, Materials & Continua. 64 (3), 1897-1913
Xiang, R., Lu, Q., Jiao, Y., Zheng, Y., Ying, W. and Long, Y., (2019). Leveraging writing systems changes for deep learning based Chinese affective analysis. International Journal of Machine Learning and Cybernetics. 10 (11), 3313-3325
Chen, I-H., Zhao, Q., Long, Y., Lu, Q. and Huang, C-R., (2019). Mandarin Chinese modality exclusivity norms. PLoS One. 14 (2), e0211336-e0211336
Chen, I-H., Long, Y., Lu, Q. and Huang, C-R., (2019). Metaphor Detection: Leveraging Culturally Grounded Eventive Information. IEEE Access. 7, 10987-10998
Zhou, J., Lu, Q., Gui, L., Xu, R., Long, Y. and Wang, H., (2019). MTTFsite: cross-cell type TF binding site prediction by using multi-task learning.. Bioinformatics. 35 (24), 5067-5077
Shen, Q., Tong, X., Long, Y. and Mao, Y., (2019). Angular Distribution of Production Planes in J/ψ→ΛΛ¯ Decay*. Chinese Physics Letters. 36 (6), 062301-062301
Li, M., Lu, Q., Xiong, D. and Long, Y., (2018). Phrase embedding learning based on external and internal context with compositionality constraint. Knowledge-Based Systems. 152, 107-116
Zhao, Q., Huang, C-R. and Long, Y., (2018). Synaesthesia in Chinese: A corpus-based study on gustatory adjectives in Mandarin. Linguistics. 56 (5), 1167-1194
Long, Y., Xiang, R., Lu, Q., Xiong, D., Huang, C-R., Bi, C. and Li, M., (2018). Learning Heterogeneous Network Embedding From Text and Links. IEEE Access. 6, 55850-55860
Li, M., Lu, Q., Long, Y. and Gui, L., (2017). Inferring Affective Meanings of Words from Word Embedding. IEEE Transactions on Affective Computing. 8 (4), 443-456
Gu, Y., Wang, D., Wang, Y., Long, Y., Jiang, S., Zhou, J. and Qu, W., (2016). Similar spatial textual objects retrieval strategy. Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis. 52 (1), 120-126
YANG, K., TAN, F., WANG, F., LONG, Y. and WEN, Y., (2012). Response Surface Optimization for Process Parameters of LiFePO4/C Preparation by Carbothermal Reduction Technology. Chinese Journal of Chemical Engineering. 20 (4), 793-802
Long, YF., Chen, YM. and Cai, TJ., (2004). Determination of deoxyribonucleic acid with nitro-tetrazolium chloride blue by resonance light-scattering technique. SPECTROSCOPY AND SPECTRAL ANALYSIS. 24 (5), 610-612
Su, JS., Chen, XM., Long, YF., Li, W. and Luo, HT., (2004). Resonance Light Scattering study on the interaction of victory blue-B with deoxyribonucleic acid and application to DNA assay. SPECTROSCOPY AND SPECTRAL ANALYSIS. 24 (1), 37-40
Long, YF., Chen, XM., Wu, QL. and Yang, WJ., (2003). Determination of deoxyribonucleic acid with alkali blue 6B by resonance light-scattering method. SPECTROSCOPY AND SPECTRAL ANALYSIS. 23 (3), 458-460
Book chapters (1)
Wang, X., Long, Y., Qin, P., Huang, C., Guo, C., Gao, Y. and Huang, C-R., (2022). From Complex Emotion Words to Insomnia and Mental Health: A Corpus-Based Analysis of the Online Psychological Consultation Discourse About Insomnia Problems in Chinese. In: Lecture Notes in Computer Science. Springer International Publishing. 221- 232. 9783031065460
Conferences (36)
Huang, G., Long, Y., Luo, C. and Li, Y., LIDA: Lexical-based Imbalanced Data Augmentation for Content Moderation
Lin, Y., Xia, Y. and Long, Y., Augmenting emotion features in irony detection with Large language modeling
Yi, P., Zubiaga, A. and Long, Y., Detecting harassment and defamation in cyberbullying with emotion-adaptive training
Lingzhi, S., Long, Y., Xiaohao, C., Guangming, C., Kang, L., Razzak, I. and Jameel, S., (2025). GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
Zhang, J. and Long, Y., (2025). MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions
Yuhan, X., Qingqing, Z., Long, Y. and Xu, G., (2024). Sensory Features in Affective Analysis: A Study Based on Neural Network Models
Huang, G., Long, Y., Luo, C., Shen, J. and Sun, X., (2024). Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process
Xia, Y., Zhao, Q., Long, Y., Wang, J. and Xu, G., (2024). SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
Friedrich, A., Huyen, NTM., Zeldes, A., Long, Y., Klinger, R., Okazaki, N., Calzolari, N., Kan, MY., Huang, CR. and Mariani, J., (2024). General Chairs' Message: LREC-COLING 2024
Shen, J., He, Y., Long, Y., Wen, J., Wang, Y. and Yang, Y., (2023). Birds of a Feather Purchase Together: Accurate Social Network Inference using Transaction Data
Zhao, Q. and Long, Y., (2022). A Diachronic Study on Linguistic Synesthesia in Chinese
Jiang, X., Zhao, Q., Long, Y. and Wang, Z., (2022). Chinese Synesthesia Detection: New Dataset and Models
Lin, Z., Liang, B., Long, Y., Dang, Y., Yang, M., Zhang, M. and Xu, R., (2022). Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis
Long, Y., Xu, H., Qi, P., Zhang, L. and Li, J., (2021). Graph Attention Network for Word Embeddings
Zhao, Q., Xiao, Y. and Long, Y., (2021). Multi-task CNN for Abusive Language Detection
Xiang, R., Chersoni, E., Long, Y., Lu, Q. and Huang, C-R., (2020). Lexical Data Augmentation for Text Classification in Deep Learning
Zhao, Q., Long, Y. and Huang, C-R., (2020). Linguistic Synaesthesia of Mandarin Sensory Adjectives: Corpus-Based and Experimental Approaches
Xiang, R., Gao, X., Long, Y., Li, A., Chersoni, E., Lu, Q. and Huang, C-R., (2020). Ciron: a New Benchmark Dataset for Chinese Irony Detection
Xiang, R., Long, Y., Wan, M., Gu, J., Lu, Q. and Huang, C-R., (2020). Affection Driven Neural Networks for Sentiment Analysis
Zhong, H., Zhou, J., Qu, W., Long, Y. and Gu, Y., (2020). An Element-aware Multi-representation Model for Law Article Prediction
Klyueva, N., Long, Y., Huang, CR. and Lu, Q., (2018). Food-related sentiment analysis for Cantonese
Long, Y., Ma, M., Lu, Q., Xiang, R. and Huang, CR., (2018). Dual Memory Network Model for Biased Product Review Classification
Long, Y., (2018). Light Meson Decays at BESIII
Long, Y., (2017). Fake News Detection Through Multi-Perspective Speaker Profiles
Chen, I-H., Long, Y., Lu, Q. and Huang, C-R., (2017). Leveraging Eventive Information for Better Metaphor Detection and Classification
Long, Y., Qin, L., Xiang, R., Li, M. and Huang, C-R., (2017). A Cognition Based Attention Model for Sentiment Analysis
Li, M., Lu, Q. and Long, Y., (2017). Representation Learning of Multiword Expressions with Compositionality Constraint
Li, M., Long, Y., Lu, Q. and Li, W., (2016). Emotion corpus construction based on selection from hashtags
Li, M., Long, Y. and Lu, Q., (2016). A regression approach to valence-arousal ratings of words from word embedding
Li, M., Wang, D., Lu, Q. and Long, Y., (2016). Event based emotion classification for news articles
Long, Y., Lu, Q., Xiao, Y., Li, M. and Huang, C-R., (2016). Domain-specific user preference prediction based on multiple user activities
Long, Y., Xiong, D., Lu, Q., Li, M. and Huang, C-R., (2016). Named Entity Recognition for Chinese Novels in the Ming-Qing Dynasties
Li, M., Lu, Q., Gui, L. and Long, Y., (2016). Towards Scalable Emotion Classification in Microblog Based on Noisy Training Data
Li, B., Long, Y. and Qu, W., (2015). Dependency parsing for Chinese long sentence: A second-stage main structure parsing method
Long, Y., Bian, Y., Qu, W. and Dai, R., (2015). New editing and checking work of the Semantic Knowledge base of Contemporary Chinese (SKCC)
Li, ML., Meng, LL., Wang, F., Su, HF., Long, YF., Wen, YX. and Yang, KD., (2011). One-step Carbothermal Reduction Synthesis of Li<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>3</sub> /C as Cathode Material for Lithium Ion Batteries
Grants and funding
2024
Iceni Economic Benefit AKT Dec 2023
Innovate UK (formerly Technology Strategy Board)
To embed new capabilities in machine learning and generative AI within Geologix, for the purpose of delivering automated actionable insights to the energy sector.
Innovate UK (formerly Technology Strategy Board)
2023
Iceni Projects Ltd
Innovate UK (formerly Technology Strategy Board)
KTP with Hood Group: To embed the latest techniques in machine learning and natural language processing for automation of data collection, collation and provision in the form of a concierge style app for travel insurance customers.
Innovate UK (formerly Technology Strategy Board)
To develop a new 'Job Discovery' Natural Language Processing (NLP) Chatbot which will help provide a superlative candidate advice and access to all potential opportunities when visiting the career page of an organisation.
Innovate UK (formerly Technology Strategy Board)
Improving multimodality misinformation detection with affective analysis
Alan Turing Institute
2022
Healthshare Limited KTP Application - Feb 2022 Submission
Innovate UK (formerly Technology Strategy Board)
2021
Horus Security KTP Application
Innovate UK (formerly Technology Strategy Board)
2020
ExTRA-PPOLATE (Explainable Therapy Related Annotations: Patient & Practitioner Oriented Learning Assisting Trust & Engagement)
Engineering and Physical Sciences Research Council
2019
Mondaq KTP 2
Innovate UK (formerly Technology Strategy Board)
Contact
Academic support hours:
Thursday 16:00-18:00
