CIC - IPN at S em E val-2025 Task 11: Transformer-Based Approach to Multi-Class Emotion Detection (2025)

Abstract: This paper presents a multi-step approach for multi-label emotion classification as our system description paper for the SEMEVAL-2025 workshop Task A using machine learning and deep learning models. We test our methodology on English, Spanish, and low-resource Yoruba datasets, with each dataset labeled with five emotion categories: anger, fear, joy, sadness, and surprise. Our preprocessing involves text cleaning and feature extraction using bigrams and TF-IDF. We employ logistic regression for baseline classification and fine-tune Transformer models, such as BERT and XLM-RoBERTa, for improved performance. The Transformer-based models outperformed the logistic regression model, achieving micro-F1 scores of 0.7061, 0.7321, and 0.2825 for English, Spanish, and Yoruba, respectively. Notably, our Yoruba fine-tuned model outperformed the baseline model of the task organizers with micro-F1 score of 0.092, demonstrating the effectiveness of Transformer models in handling emotion classification tasks across diverse languages.

## introduction emotions play a crucial role in human communication, shaping our interactions, decisions, and psychological well-being. according to the oxford english dictionary, emotion is defined as "a strong feeling deriving from one's circumstances, mood, or relationships with others." in social interactions, emotions are frequently invoked and help individuals navigate complex relationships and make sense of their environments @xcite . in text, emotions can be conveyed explicitly or implicitly through linguistic patterns, allowing authors to communicate their mental states. consequently, emotion classification-identifying and labeling the emotions embedded in text-has become a key research area in natural language processing (nlp), with applications across various domains such as marketing, healthcare, and education. while sentiment analysis focuses on determining the overall emotional tone (positive, negative, or neutral) of a text, emotion classification goes a step further by identifying specific emotions such as anger, joy, fear, or sadness. this task is particularly challenging when multiple emotions are present simultaneously in a single text, a problem known as multi-label emotion classification. unlike traditional single-label emotion classification, where only one emotion label is associated with a given statement, multi-label classification assigns multiple emotion labels to a text, reflecting the complex nature of human emotional expression. multi-label emotion classification faces numerous challenges, particularly in the realm of social media, where the language evolves rapidly and the context is often ambiguous. to address these challenges, researchers have turned to deep learning models, especially recurrent neural networks (rnns), long short-term memory (lstm) networks, and attention mechanisms, which have shown promising results in recent years. these models can capture nuanced emotional expressions by learning from large-scale datasets, such as the semeval-2018 task-1 and the blog emotion corpus, which contain emotional annotations for a variety of social media texts. however, despite advancements in deep learning, existing models still face limitations in capturing the full range of emotional nuances in multi-label classification tasks. moreover, traditional machine learning methods often struggle with feature engineering, making them less adaptable to rapidly changing language used on platforms like twitter. in light of these challenges, this paper explores transformer approaches to multi-label emotion detection by leveraging transfer learning and multiattention mechanisms. by fine-tuning pre-trained models such as bert and xlmroberta, along with incorporating feature engineering to capture emotion-specific features, we aim to enhance the accuracy and robustness of emotion classification systems. in the following sections, we first review the related work on emotion classification, particularly in multi-label contexts. then, we describe our methodology, which includes data preprocessing, model architecture, and experimental setup. finally, we present our experimental results, demonstrating the effectiveness of our approach in english, spanish and yoruba emotion classification tasks. ## recent literature text detection and classification has taken several forms and gained attention by researchers over the last couple of years in nlp, with the application of different classifiers and models, also some more accurate models being developed by researchers, performing significant roles in the series of experiments that have been undertaken in recent work @xcite @xcite @xcite . a variety of traditional ml methods @xcite @xcite @xcite @xcite @xcite have been applied in the last few years for text prediction on various domains. most previous work on emotion detection has focused on deep neural networks such as convolutional neural networks (cnns) and recurrent neural networks (rnns) like long short-term memory (lstm) networks. however, these models have not utilized multiple attention mechanisms or transformer-based networks such as xlnet, distilbert, and roberta for multi-label emotion classification. @xcite proposed multiple attention mechanisms to reveal the contribution of each word to each emotion, which had not been investigated before. their roberta-multi-attention (roberta-ma) model achieved 62.4% accuracy, outperforming the previous stateof-the-art accuracy of 58.8% on the semeval-2018 task-1c dataset. similarly, their xlnet-ma model achieved 45.6% accuracy on the ren-cecps dataset for chinese, demonstrating the effectiveness of transformer-based models in multi-label emotion classification. @xcite conducted a psycholinguistic and emotional analysis of cryptocurrency discussions on social media, focusing on nine major digital assets, including bitcoin, ethereum, and dogecoin. using advanced text analysis techniques, the study examined linguistic patterns and emotional expressions across different cryptocurrency communities. the authors also analyzed comentions among cryptocurrencies to understand their interrelations. a dataset of 832,559 tweets was collected and refined to 115,899 for analysis, providing insights into the distinct discourse surrounding each coin and the emotional trends shaping cryptocurrency discussions online. speech emotion recognition (ser) plays a crucial role in enhancing human-computer interaction (hci) by enabling machines to understand human emotions from acoustic signals. however, the lack of large-scale datasets remains a major challenge in this field. banspemo: a bangla audio dataset for speech emotion recognition and its baseline evaluation @xcite addresses this issue by introducing banspemo, a bangla speech emotion dataset comprising 792 recordings from 22 native speakers, covering six emotions: disgust, happiness, anger, sadness, surprise, and fear. the study evaluates baseline models, including support vector machine (svm), logistic regression (lr), and multinomial naïve bayes, finding that svm achieves the highest accuracy of 87.18%. inspired by this work, transformer-based models provide an advanced approach to ser by leveraging deep contextual representations for improved multiclass emotion detection across languages. this study builds upon such datasets to enhance crosslinguistic emotion classification using transformer architectures. emotion detection in online communication has been extensively studied, but many approaches focus solely on textual cues, overlooking the role of emojis in conveying emotions. multimodal text-emoji fusion using deep neural networks for text-based emotion detection in online communication @xcite highlights the significance of incorporating emoji analysis to improve sentiment interpretation, especially in cases where text alone may not fully capture emotional intent. the study proposes an emoji-aware hybrid deep learning framework that leverages convolutional and recurrent neural networks for multimodal emotion detection. inspired by this, transformer-based models offer a promising approach to crosslinguistic multi-class emotion detection, as they can capture deep contextual relationships in multimodal data. this study builds on such insights by integrating transformer architectures for improved emotion classification in diverse linguistic settings. ## methodology our methodology involves a multi-step approach to text preprocessing, feature extraction, and multilabel emotion classification using machine learning and deep learning models. experiments were conducted on english and spanish datasets to test our method across different languages, including the low-resource language yoruba. ## conclusion the experiment demonstrated the effectiveness of transformer models over traditional machine learning for multi-label emotion classification. the addition of bigram features enhanced feature representation for logistic regression and the deep learning figure 3: confusion matrix of the spanish dataset prediction figure 4: roc of the spanish dataset prediction figure 5: confusion matrix of the yoruba dataset prediction figure 6: roc of the yoruba dataset prediction