NLP _goats at S em E val-2025 Task 11: Multi-Label Emotion Classification Using Fine-Tuned Roberta-Large Tranformer (2025)

Abstract: This paper serves as a solution for multi-label emotion classification and intensity for text, developed for SemEval-2025 Task 11. The method uses a fine-tuned RoBERTa-Large transformer model. The system represents a multi-label classification approach to identifying multiple emotions, and uses regression models to estimate emotion strength. The model performed with ranks of 31st and 17th place in the corresponding tracks. The findings show impressive performance and it remains possible to improve the performance of ambiguous or low-frequency emotion recognition using the state-of-the-art contextual embeddings and threshold optimization techniques.

## introduction text-based emotion recognition has become one of the core elements of contemporary natural language processing, which has changed the way we perceive and use digital communication. today's social media and instant messaging culture often allows people to communicate their nuanced, often unacknowledged, emotional states through the written word. this rich tapestry of affective expression not only influences personal interactions but also drives applications in customer service, mental health monitoring, and social analytics. nevertheless, conventional sentiment analysis methods usually lack the depth of human emotion spectrum and nuances. track 1 is concerned with multi-label emotion detection and track 2 is concerned with emotion intensity estimation. track 1 promotes the creation of models able to detect shared emotional states in a text, accommodating the complex dimension of human affect. simultaneously, track 2 asks the researchers to measure the fine nuances in the intensity of emotion, beyond dichotomizing between emotion intensities, to provide a more nuanced description of affective expression. taken together, these tasks strive for the current state-of-the-art by encouraging novel solutions that can cope with the complexity and richness of real-world textual emotions. this paper is structured as follows: section 2 surveys the relevant related works in textual emotion detection and sentiment analysis, discussing both historical trends and recent progress. section 3 gives a detailed explanation/ task description such as the dataset and the evaluation metrics. section 4 consists of the adopted methodology, highlighting the preprocessing techniques, model architectures, and training procedures. section 5 presents the experimental results along with a comparative analysis of the approaches used. section 6 discusses the error analysis, to identify potential areas for improvement. finally, section 7, the conclusion section, contains a summary of key findings and insights into future research directions. by addressing these challenges, the gap between conventional sentiment analysis methods and the complex, multidimensional nature of human emotion is bridged. through advanced textual analysis and innovative modeling techniques, the aim is to enhance the reliability and depth of emotion detection systems, ultimately contributing to more empathetic and effective digital communication platforms. the code for the tasks is available on github at this repository. ## related work emotion analysis on textual data has been extensively researched in natural language processing (nlp) with applications from multi-label emotion classification to intensity regression of emotions. conventional methods were based on lexiconbased approaches, in which words were assigned to pre-defined emotional categories in terms of resources like the nrc emotion lexicon (mohammad and turney, 2013). while these approaches provided useful insights, they lacked contextual understanding and struggled with complex linguistic patterns. multi-label emotion classification is to assign multiple emotion labels to a text. early mlbased approaches have been applied to tf-idf and n-gram features with support vector machines (svms) and random forests @xcite , respectively. nevertheless, the traditional methods have been surpassed by deep learning methods like long short-time memory (lstm) networks and convolutional neural networks (cnns) @xcite . the introduction of the transformers, especially bert @xcite and roberta @xcite , has improved multi-label classification considerably by exploiting the contextual embeddings. recent works have optimized thresholding techniques, such as adaptive thresholding @xcite and focal loss, to handle label imbalances in multi-label classification. emotion intensity prediction (i.e., how much of an emotional expression is there in a given text) has been addressed from both lexicon-based and deep-learning points of view. early works relied on affective lexicons such as the nrc affect intensity lexicon (mohammad, 2018) to assign predefined intensity scores. nevertheless, those approaches could not learn the dynamic emotion expression in real-world text. recurrent neural networks (rnns) and bilstms have been applied to model sequential dependencies in text, improving intensity prediction @xcite . transformer-based architectures (e.g., bert, roberta) have also significantly helped this domain by training models to perform regression by tuning the model parameters for regression tasks using loss functions such as mean squared error (mse) or huber loss @xcite . in this paper, we focus both on the multi-label emotion classification and the emotion intensity regression, both using transformer-based models. the classification problem is approached as a multilabel problem, using binary cross-entropy with logits (bcewithlogitsloss) and threshold-tuning methods to enhance emotion detection. in the regression task, the model is trained to predict the intensity of emotion by mse loss, with the goal of optimal fine-grained emotion strength detection. by integrating recent advancements in transformers and loss function optimization, this work aims to enhance both the classification and regression aspects of emotion analysis in textual data. ## task description we focus on two related yet distinct tasks aimed at analyzing the emotional content in text: multilabel emotion detection and emotion intensity prediction. both tasks contribute to a deeper understanding of affective computing, particularly in the context of social media, dialogues, and opinionated text. the dataset is due to the efforts of @xcite and @xcite . ## methodology this paper aims at two fundamental tasks of emotion analysis: emotion classification with multiple labels and regression of emotion intensity. we adopt transformer-based models, i.e., fine-tuned bert and roberta models, to better solve both tasks. the methodology is composed of data preprocessing, model structure, training approach, and evaluation metrics. ## conclusion track a and track b results demonstrate strong performance in classification and regression tasks. in track a, the model achieved a macro f1 score of 0.75, which indicates a well-balanced performance across all emotion categories. the micro f1 score of 0.7749 suggests that the model handles overall classification instances effectively. the model in track b achieved a mean pearson correlation value of 0.7508, indicating very high correspondence between predicted and ground truth emotion intensity. the figures indicate the model's power in both the classification of discrete emotion and prediction of continuous intensity and its strength in dealing with sensitive emotional expressions from the text.