RSSN at S em E val-2025 Task 11: Optimizing Multi-Label Emotion Detection with Transformer-Based Models and Threshold Tuning (2025)

Abstract: Our study explores multi-label emotion classification using fine-tuned BERT models, achieving superior performance over traditional methods such as logistic regression. The intricate nature of overlapping emotional expressions in text necessitates a robust classification framework. Fine-tuning BERT with weighted binary cross-entropy loss enhances predictive accuracy, particularly for underrepresented emotions like anger and joy. Moreover, threshold optimization plays a pivotal role in refining decision boundaries, boosting recall, and increasing the macro F1-score. Comparative analysis against RoBERTa and XGBoost further underscores the effectiveness of contextual embeddings in capturing subtle emotional nuances. Despite these improvements, challenges such as class imbalance and inter-class confusion persist, highlighting the need for future advancements in ensemble learning, contrastive pretraining, and domain-adaptive fine-tuning.

## introduction emotion classification in nlp is essential for identifying perceived emotions, which reflect how an audience interprets a speaker's sentiment. unlike sentiment analysis, which categorizes text as positive, negative, or neutral, multi-label emotion detection captures multiple co-occurring emotions in a single instance. this study focuses on semeval 2025 (track a) for multi-label emotion detection in english, classifying text snippets into six perceived emotions: joy, sadness, anger, fear, surprise, and disgust. rather than identifying the speaker's true emotions or reader's reactions, the task centers on commonly inferred emotions, influenced by linguistic and cultural factors. we fine-tune a bert-based model to capture contextual dependencies while addressing class imbalance and label co-occurrence challenges. our approach includes text preprocessing with spacy, dataset analysis, and hyperparameter tuning. tf-idf-based models serve as baselines, and weighted binary cross-entropy loss is used for training. performance is evaluated via f1-score, the official metric for the task. experimental results show that transformerbased models significantly outperform traditional methods, effectively detecting multiple emotions per instance. despite improvements, challenges like inter-class confusion and label ambiguity remain. this study provides insights into optimizing multi-label emotion classification and outlines directions for future research. exploring subjectivity and sentiment analysis, wiebe et al. @xcite introduced a detailed corpus annotation framework. their methodology focused on phrase-level subjectivity rather than sentence-level labels, incorporating nuanced elements such as private states, beliefs, and nested sources of emotion. the framework has since been widely adopted for opinion mining and sentiment classification tasks. 3 system overview ## baseline approaches to establish a foundational benchmark for multilabel emotion classification, we experimented with logistic regression models trained on tf-idf representations of text. these models were selected due to their efficiency and interpretability in text classification tasks. multiple variations were explored to examine the impact of feature engineering, class imbalance handling, and threshold optimization. ## advanced implementations and experimental analysis to tackle multi-label emotion classification, we explored various deep learning approaches, leveraging pre-trained transformers and hybrid architectures integrating tf-idf and cnns. ## results and performance evaluation this section presents a comprehensive analysis of the results obtained from our experiments on multilabel emotion classification. the models were finetuned on the preprocessed dataset and evaluated based on key performance metrics, including macro f1-score, precision, recall, and accuracy. additionally, we conducted threshold optimization to enhance the classification performance. the results are consolidated in the following subsections.