Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.
Challenges for Open-domain Targeted Sentiment Analysis
Westlake University（西湖大学）；Westlake Institute for Advanced Study（西湖高等研究院）；Nanjing University of Science and Technology（南京理工大学）；National University of Singapore（新加坡国立大学）
Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；The 30th Research Institute of China Electronics Technology Group Corporation（中国电子科技集团第三十研究所）；Nanyang Technological University（南洋理工大学）
A Spanish dataset for Targeted Sentiment Analysis of political headlines
Instituto de Fisiología, Biología Molecular y Neurociencia, CONICET, UBA（布宜诺斯艾利斯大学国家科学技术研究委员会生理、分子生物学与神经科学研究所）；Facultad de Psicología, UBA（布宜诺斯艾利斯大学心理学学院）；Instituto de Ciencias de la Computación, CONICET, UBA（布宜诺斯艾利斯大学国家科学技术研究委员会计算机科学研究所）
FCKT: Fine-Grained Cross-Task Knowledge Transfer with Semantic Contrastive Learning for Targeted Sentiment Analysis
School of Artificial Intelligence, Beihang University, China（北京航空航天大学人工智能学院）；School of Computer Science and Engineering, Beihang University, China（北京航空航天大学计算机科学与工程学院）；Xidian University, China（西安电子科技大学）；Zhongguancun Laboratory, China（中关村实验室）
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis
Stanford University（斯坦福大学）；National University of Singapore（新加坡国立大学）；Institute of High Performance Computing, Agency for Science, Technology, and Research（新加坡科技研究局高性能计算研究所）
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning
Xiamen University（厦门大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Rochester（罗切斯特大学）；Tianjin University（天津大学）
Breaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted Misclassification
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology（韦洛尔理工学院计算机科学工程与信息系统学院）；Center for AI Research (CAIR), University of Agder(UiA)（阿格德大学人工智能研究中心）；College of Computer Science and Information Technology, IAU（IAU计算机科学与信息技术学院）
The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
School of Business and Management, Queen Mary University of London（女王玛丽大学商学院）；Wolfson Institute of Population Health (WIPH), Queen Mary University of London（人口健康沃尔夫森研究所）；Department of Medicine, University of Nottingham（诺丁汉大学医学部）；Department of Informatics, Systems, and Communication, University of Milano-Bicocca（米兰-比科卡大学信息学、系统与通信系）
From Sentiment Classification to Actionable and Responsible Feedback: A Scoping Review and Evidence Map of NLP in Student Evaluation of Teaching, 2015-2026
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Zhejiang University（浙江大学）；Tencent（腾讯）；Microsoft Dynamics 365 AI Research（微软Dynamics 365人工智能研究院）
IndoBERT-Relevancy: A Context-Conditioned Relevancy Classifier for Indonesian Text
SocialX；Center of Excellence SAKTI, Research Institute Intelligent Business & Sustainable Economy, Telkom University（卓越中心SAKTI，智能商业与可持续经济研究所，特尔康大学）；Biomedical Engineering Study Program, School of Electrical Engineering, Telkom University（生物医学工程专业，电气工程学院，特尔康大学）；Research Center for Smart Mechatronics, National Research and Innovation Agency (BRIN)（智能机电一体化研究中心，国家研究与创新署（BRIN））
