Existing approaches to fine-grained emotion classification (FEC) often operate in Euclidean space, where the flat geometry limits the ability to distinguish semantically similar emotion labels (e.g., *annoyed* vs. *angry*). While prior research has explored hyperbolic geometry to capture fine-grained label distinctions, it typically relies on predefined hierarchies and ignores semantically similar negative labels that can mislead the model into making incorrect predictions. In this work, we propose HyCoEM (Hyperbolic Contrastive Learning for Emotion Classification), a semantic alignment framework that leverages the Lorentz model of hyperbolic space. Our approach embeds text and label representations into hyperbolic space via the exponential map, and employs a contrastive loss to bring text embeddings closer to their true labels while pushing them away from adaptively selected, semantically similar negatives. This enables the model to learn label embeddings without relying on a predefined hierarchy and better captures subtle distinctions by incorporating information from both positive and challenging negative labels. Experimental results on two benchmark FEC datasets demonstrate the effectiveness of our approach over baseline methods.

Recent approaches to Hierarchical Text Classification (HTC) rely on capturing the global label hierarchy, which contains static and often redundant relationships. Instead, the hierarchical relationships within the instance-specific set of positive labels are more important, as they focus on the relevant parts of the hierarchy. These localized relationships can be modeled as a semantic alignment between the text and its positive labels within the embedding space. However, without explicitly encoding the global hierarchy, achieving this alignment directly in Euclidean space is challenging, as its flat geometry does not naturally support hierarchicalrelationships. To address this, we propose Hyperbolic Instance-Specific Local Relationships (HyILR), which models instance-specific relationships using the Lorentz model of hyperbolic space. Text and label features are projected into hyperbolic space, where a contrastive loss aligns text with its labels. This loss is guided by a hierarchy-aware negative sampling strategy, ensuring the selection of structurally and semantically relevant negatives. By leveraging hyperbolic geometry for this alignment, our approach inherently captures hierarchical relationships and eliminates the need for global hierarchy encoding. Experimental results on four benchmark datasets validate the superior performance of HyILR over baseline methods.

Our proposed work introduces a novel approach to privacy-preserving federated learning market basket analysis using Homomorphic encryption. By encrypting frequent mining operations using Homomorphic encryption, our method ensures data privacy without compromising analysis efficiency. Experiments on diverse datasets validate its effectiveness in maintaining data integrity while preserving privacy.

This paper outlines the methodology for the automatic extraction of self-reported ages from social media posts as part of the Social Media Mining for Health (SMM4H) 2024 Workshop Shared Tasks. The focus was on Task 6: “Self-reported exact age classification with cross-platform evaluation in English.” The goal was to accurately identify age-related information from user-generated content, which is crucial for applications in public health monitoring, targeted advertising, and demographic research. A number of transformer-based models were employed, including RoBERTa-Base, BERT-Base, BiLSTM, and Flan T5 Base, leveraging their advanced capabilities in natural language understanding. The training strategies included fine-tuning foundational pre-trained language models and evaluating model performance using standard metrics: F1-score, Precision, and Recall. The experimental results demonstrated that the RoBERTa-Base model significantly outperformed the other models in this classification task. The best results achieved with the RoBERTa-Base model were an F1-score of 0.878, a Precision of 0.899, and a Recall of 0.858.