D ata B ees at S em E val-2025 Task 11: Challenges and Limitations in Multi-Label Emotion Detection (2025)

Abstract: Text-based emotion detection is crucial in NLP,with applications in sentiment analysis, socialmedia monitoring, and human-computer interaction. This paper presents our approach tothe Multi-label Emotion Detection challenge,classifying texts into joy, sadness, anger, fear,and surprise. We experimented with traditionalmachine learning and transformer-based models, but results were suboptimal: F1 scores of0.3723 (English), 0.5174 (German), and 0.6957(Spanish). We analyze the impact of preprocessing, model selection, and dataset characteristics, highlighting key challenges in multilabel emotion classification and potential improvements.

## introduction emotion detection from text is a crucial nlp task with applications in customer feedback analysis, mental health detection, and social media monitoring. unlike sentiment analysis, which determines polarity, emotion detection classifies text into joy, sadness, fear, anger, and surprise, often requiring multi-label classification since a sentence can evoke multiple emotions. despite advancements in transformer-based models like bert and xlm-roberta, challenges remain due to: ## dataset our dataset comes from task 11 of semeval 2025 @xcite , focusing on multi-label emotion classification in english, german, and spanish. sourced from social media, each text snippet is annotated using a binary scheme (1: present, 0: absent), allowing for multi-label classification where multiple emotions can co-occur. neutral instances contain no marked emotions. the dataset is split into train, dev, and test sets for structured evaluation. ## related works emotion detection in textual data has been extensively explored in nlp, spanning lexicon-based, machine learning, and deep learning methods. transformer-based models have recently set new benchmarks, particularly for multi-label and multilingual emotion classification. ## methodology the steps taken are laid out in the methodology section in detail, with the inclusion of preprocessing, exploratory data analysis (eda), model selection, evaluation, and the implementation of recommendations received while conducting the study. ## conclusion our experiments highlight key challenges in multilabel emotion detection. the lower performance in english suggests that pre-processing techniques may have removed valuable contextual information. additionally, the choice of separate binary classifiers for each emotion may have hindered the model's ability to distinguish between overlapping emotional expressions. our findings suggest that future research should focus on retaining more textual information during pre-processing, implementing multi-class classification rather than binary classifiers for each emotion and exploring larger, domain-specific pre-trained transformer models with better fine-tuning strategies. by addressing these factors, we can improve the accuracy and reliability of emotion detection in text across multiple languages.