C harsiu R ice at S em E val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (2025)

Abstract: This paper presents our participation in SemEval-2025 Task 11, which focuses on bridging the gap in text-based emotion detection. Our team took part in both Tracks A and B, addressing different aspects of emotion classification. We fine-tuned a RoBERTa base model on the provided dataset in Track A, achieving a Macro-F1 score of 0.7264. For Track B, we built on top of the Track A model by incorporating an additional non-linear layer, in the hope of enhancing Track A model’s understanding of emotion detection. Track B model resulted with an average Pearson’s R of 0.5658. The results demonstrate the effectiveness of fine-tuning in Track A and the potential improvements from architectural modifications in Track B for emotion intensity detection tasks.

## introduction emotion is defined as "a complex reaction pattern, involving experiential, behavioral, and physiological elements, by which an individual attempts to deal with a personally significant matter or event" @xcite . humans express emotions through speech and behavior, but recognizing and empathizing with emotions is not always straightforward, as emotions are abstract and subjective. in the digital era, emotion detection is increasingly valuable in applications such as chatbots and ai writing assistants. @xcite challenges participants to detect emotions and their intensities from the speaker's perspective across multiple languages. we focused on english due to team familiarity and early dataset availability. the task includes three tracks. track a targets multi-label classification of perceived emotions; track b predicts their intensities; and track c extends emotion detection to an unseen target lan-guage using a model trained only on english. our submission covers track a and track b. ## system overview as briefly explained previously, we opted for the bert models. to cater both track a (multi-label emotion classification) and track b (multi-emotion intensity level estimation) tasks. given the related nature of tracks a and b, we adopted a unified training approach to take advantage of semantic overlap between the two tasks. in particular, the model was first fine-tuned on track a data, which focuses on classifying the presence of emotions inside the text. afterwards the model training is followed by another fine-tuning on track b data, which involves estimating the intensity of each emotion. this sequential training allowed the model to build a rich understanding of emotion-related features and semantic information from track a before further refining its ability to handle emotion intensity nuances in track b. ## experimental setup all experiments were conducted on google colab using gpu runtime. the software environment included python 3.8, pytorch 1.12.0, and the hugging face transformers library @xcite . to ensure reproducibility, we fixed the random seed to 42 and documented all preprocessing steps and hyperparameters. ## results and analysis this section reports on our models' performances on the test sets of track a and b, plus analysis of results and system errors. about the official evaluation metrics, track a uses marco-f1 score between model prediction and gold labels, while the average pearson's r over language-specific emotions is the metric of track b's performance. jaccard index for track a and mean absolute error (mae) for track b are added for more in-depth understanding of models' performance. ## conclusion we participated in tracks a and b of the shared task by fine-tuning bert-based models for multilabel emotion detection and emotion intensity prediction on english texts. our model ranked 44th in track a-around the median-but underperformed compared to the baseline in track b. the results highlight the challenges of emotion detection, even in a high-resource language, as emotions are often implicit and not directly conveyed through surface-level text. they also suggest that full fine-tuning may not be optimal given the dataset size and distribution. we hope our work offers insights for future research and the development of emotion-aware applications using pre-trained language models.