Textual emotion recognition has been a promising research topic in recent years. Many researchers aim to build more accurate and robust emotion detection systems. In this paper, we conduct several experiments to indicate how data pre-processing affects a machine learning method on textual emotion recognition. These experiments are performed on the Vietnamese Social Media Emotion Corpus (UIT-VSMEC) as the benchmark dataset. We explore Vietnamese social media characteristics to propose different pre-processing techniques, and key-clause extraction with emotional context to improve the machine performance on UIT-VSMEC. Our experimental evaluation shows that with appropriate pre-processing techniques based on Vietnamese social media characteristics, Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40%, a significant improvement of 4.66% over the CNN model built by the authors of UIT-VSMEC (59.74%).
ViTOED: A Dataset for Target-Oriented Emotion Detection on Vietnamese Social Media Texts
Faculty of Information Science and Engineering, University of Information Technology（信息科学与工程学院，信息科技大学）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
Emotion Recognition for Vietnamese Social Media Text
University of Information Technology, VNU-HCM（胡志明市信息技术大学（VNU-HCM））；Vietnam National University, Ho Chi Minh City（越南胡志明市国家大学）；University of Social Sciences and Humanities, VNU-HCM（胡志明市社会科学与人文大学（VNU-HCM））；Multimedia Communications Laboratory, University of Information Technology, VNU-HCM（胡志明市信息技术大学多媒体通信实验室（VNU-HCM））
Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition
University of Information Technology, Ho Chi Minh City, Vietnam（越南胡志明市信息技术大学）；Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam（越南胡志明市国家大学）
VLSP 2023 -- LTER: A Summary of the Challenge on Legal Textual Entailment Recognition
Vietnam Judicial Academy（越南司法学院）；Hanoi Law University（河内法律大学）；Hung Yen University of Technology and Education（兴安工业大学）；Ho Chi Minh City University of Science（胡志明市科学大学）；Japan Advanced Institute of Science and Technology（日本先进科学技术学院）；The Institute of Statistical Mathematics（统计数学研究所）；National Institute of Informatics（情报学研究所）
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations
LIRMM UMR 5506；CNRS（法国国家科学研究中心）；University of Montpellier（蒙彼利埃大学）；IUT de Béziers（贝济耶大学技术学院）；AMIS；Paul Valery University - Montpellier 3（保罗·瓦莱里大学-蒙彼利埃第三大学）
ViHateT5: Enhancing Hate Speech Detection in Vietnamese With A Unified Text-to-Text Transformer Model
Faculty of Information Science and Engineering, University of Information Technology（信息科学与工程学院，信息科技大学）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）
HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Good
Umeå University（于默奥大学）；The Australian E-Health Research Centre, CSIRO（澳大利亚电子健康研究中心，联邦科学与工业研究组织）；University of Engineering and Technology (UET), Vietnam National University, Hanoi（河内国家大学工程技术大学）；InfoRE Technology Company（InfoRE技术公司）；Hanoi University of Science (HUS), Vietnam National University, Hanoi（河内国家大学自然科学大学）
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（南洋理工大学-英属哥伦比亚大学联合卓越研究中心（老年人积极生活方向））；Alibaba-NTU Singapore Joint Research Institute（阿里巴巴-南洋理工大学新加坡联合研究院）；Nanyang Technological University（南洋理工大学）
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST)（华中科技大学人工智能与自动化学院）；Institute of Artificial Intelligence, HUST（华中科技大学人工智能研究院）；Hubei Key Laboratory of Brain-inspired Intelligent Systems, HUST（华中科技大学湖北省脑启发智能系统重点实验室）；Key Laboratory of Image Processing and Intelligent Control (HUST), Ministry of Education（华中科技大学图像信息处理与智能控制教育部重点实验室）
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Imperial College London（伦敦帝国学院）；Bayero University Kano（卡诺大学）；Cardiff University（卡迪夫大学）；Data Science for Social Impact, University of Pretoria（社会影响数据科学，开普敦大学）；University of Göttingen（哥廷根大学）；Uppsala University（乌普萨拉大学）；University of Melbourne（墨尔本大学）；IIIT Hyderabad（海得拉巴印度理工学院）；University of Alberta（阿尔伯塔大学）；Northeastern University（东北大学）；MILA（蒙特利尔人工智能实验室）；McGill University（麦吉尔大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）；MBZUAI（马斯喀特人工智能研究所）；LIACC, FEUP, University of Porto（LIACC，FEUP，波尔图大学）；Sailplane AI（Sailplane人工智能）；University of Santiago de Compostela（圣地亚哥-德孔波斯特拉大学）；University of Bucharest（布加勒斯特大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；SADiLaR；University of York（约克大学）；Universidad Politécnica Salesiana（萨利纳斯理工大学）；Lancaster University（兰卡斯特大学）；Technical University of Munich（慕尼黑技术大学）；Hamburg University（汉堡大学）；Al Akhawayn University（阿尔阿赫瓦因大学）
Dimensional emotion recognition using visual and textual cues
Faculty of Engineering of University of Porto (FEUP)（波尔图大学工程学院）；INESC TEC（波尔图工程学院科技与高等研究中心）；Faculty of Science of University of Porto (FCUP)（波尔图大学理学院）；UCP-ESB（葡萄牙天主教大学商业经济学院）
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST)（华中科技大学人工智能与自动化学院）；Institute of Artificial Intelligence, Huazhong University of Science and Technology (HUST)（华中科技大学人工智能研究院）；Key Laboratory of Image Processing and Intelligent Control, Ministry of Education（教育部图像信息处理与智能控制重点实验室）；Hubei Key Laboratory of Brain-inspired Intelligent Systems（湖北省类脑智能系统重点实验室）
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
National University of Singapore（新加坡国立大学）；Instituto Politécnico Nacional（墨西哥国立理工学院）；Nanyang Technological University（南洋理工大学）；University of Michigan（密歇根大学）
Modality-Transferable Emotion Embeddings for Low-Resource Multimodal Emotion Recognition
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；Department of Electronic and Computer Engineering（电子与计算机工程系）；The Hong Kong University of Science and Technology（香港科技大学）
Fusing Audio, Textual and Visual Features for Sentiment Analysis of News Videos
Engineering and Technology Institute, University Center of Belo Horizonte (UNI-BH)（贝洛奥里藏特大学中心（UNI-BH）工程技术学院）；CEFET-MG（米纳斯吉拉斯联邦教育技术中心（CEFET-MG））；Federal University of Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学（UFMG））
Relational graph-driven differential denoising and diffusion attention fusion for multimodal conversation emotion recognition
College of Computer and Mathematics, Central South University of Forestry and Technology（中南林业科技大学计算机与数学学院）；Department of Computer Science, State University of New York, New Paltz（纽约州立大学新帕尔茨分校计算机科学系）
