Dr Yunfei Long

School of Computer Science and Electronic Engineering (CSEE)
Dr Yunfei Long
  • Email

  • Location

    4B.521, Colchester Campus

  • Academic support hours

    Thursday 16:00-18:00



My research is focused on natural language understanding, currently, I have three research threads: 1: Applying linguistic knowledge bases to fundamental NLP tasks. 2: NLP applications in healthcare, legal, criminology, and media. 3: User profile-based emotion analysis based on text and multimodality data.


  • 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.

Key words: Explainable AI
Open to supervise

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.

Key words: Affective analysis
Open to supervise

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.

Open to supervise


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 (30)

Zhao, Q., Long, Y., Jiang, X., Wang, Z., Huang, C-R. and Zhou, G., Linguistic Synesthesia Detection: Leveraging Culturally Enriched Linguistic Features. Natural Language Processing (previously Natural Language Engineering)

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

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, 1-21

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

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

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 (30)

Shen, J., He, Y., Long, Y., Wen, J., Wang, Y. and Yang, Y., Birds of a Feather Purchase Together: Accurate Social Network Inference using Transaction Data

Huang, G., Long, Y., Luo, C. and Li, Y., LIDA: Lexical-based Imbalanced Data Augmentation for Content Moderation

Huang, G., Long, Y., Luo, C., Shen, J. and Sun, X., 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., SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification

Lin, Y., Xia, Y. and Long, Y., Augmenting emotion features in irony detection with Large language modeling

Yuhan, X., Qingqing, Z., Long, Y. and Xu, G., (2024). Sensory Features in Affective Analysis: A Study Based on Neural Network Models

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., (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)

Grants and funding


Iceni Economic Benefit AKT Dec 2023

Innovate UK (formerly Technology Strategy Board)


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


Healthshare Limited KTP Application - Feb 2022 Submission

Innovate UK (formerly Technology Strategy Board)


Horus Security KTP Application

Innovate UK (formerly Technology Strategy Board)


ExTRA-PPOLATE (Explainable Therapy Related Annotations: Patient & Practitioner Oriented Learning Assisting Trust & Engagement)

Engineering and Physical Sciences Research Council


Mondaq KTP 2

Innovate UK (formerly Technology Strategy Board)



4B.521, Colchester Campus

Academic support hours:

Thursday 16:00-18:00

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