AKCIT at S em E val-2025 Task 11: Investigating Data Quality in P ortuguese Emotion Recognition (2025)

Abstract: This paper investigates the impact of data quality and processing strategies on emotion recognition in Brazilian Portuguese (PTBR) texts. We focus on data distribution, linguistic context, and augmentation techniques such as translation and synthetic data generation. To evaluate these aspects, we conduct experiments on the PTBR portion of the BRIGHTER dataset, a manually curated multilingual dataset containing nearly 100,000 samples, of which 4,552 are in PTBR. Our study encompasses both multi-label emotion detection (presence/absence classification) and emotion intensity prediction (0 to 3 scale), following the SemEval 2025 Track 11 setup. Results demonstrate that emotion intensity labels enhance model performance after discretization, and that smaller multilingual models can outperform larger ones in low-resource settings. Our official submission ranked 6th, but further refinements improved our ranking to 3rd, trailing the top submission by only 0.047, reinforcing the significance of a data-centric approach in emotion recognition.

## introduction data quality plays a critical role in enabling machine learning models to generalize effectively and generate meaningful predictions @xcite . on the other hand, poor data quality (e.g., a high level of noise, inconsistencies, imbalanced distributions, or annotation errors) can lead to biased models and unreliable outcomes @xcite . several studies have shown that wellconstructed datasets significantly enhance model performance in nlp tasks @xcite . in the context of emotion recognition, high-quality data is essential for capturing subtle emotional nuances and reflecting variations across different contexts and linguistic structures. in this paper, we investigate the impact of data quality on emotion recognition in brazilian por-tuguese (ptbr) texts, focusing on data distribution, linguistic context (e.g., word-sentiment lexicons), and augmentation strategies such as translation and synthetic data generation. to evaluate these aspects, we conduct experiments on the ptbr portion of brighter dataset @xcite , a multilingual large-scale dataset manually curated comprissing nearly 100,000 samples, which 4,552 are in ptbr. the dataset originates from semeval 2025 track 11 @xcite , and we participate in both sub-track a (multi-label emotion detection; classifying either if the emotion is or is not present in the sentence) and sub-track b (emotion intensity; classifying the intensity from 0 to 3 of the emotion in the sentence) sub-tracks. our findings highlight the importance of a data-centric approach, measuring the impact of different data processes alternatives, as the official submission achieved 6th in portuguese emotion recognition. however, further analysis and post-competition refinements established a new 3rd place ranking, trailing the top-ranked submission by only 0.047. our main contributions can be summarized as follows: the rest of this paper is organized as follows: section 2, reviews the related work. section 3 describes the methods and section 4 the experimental setup, while experiments results are discussed in section 5. section 6 brings the conclusions. ## related work sentiment analysis has been widely studied across various domains, including movie @xcite and product reviews @xcite , with social media platforms also receiving significant attention @xcite due to the vast amount of user-generated content. @xcite propose a bidirectional lstms with attention mechanisms to predict sentiment polarity, categorizing tweets as positive, negative, or neutral. their model achieved first place in semeval-2017 task 4. however, since it follows a single-label classification approach, it cannot effectively capture the overlapping and co-occurring emotions often present in social media text. to address the challenges of multilabel emotion classification, @xcite proposed an attention-based convolutional neural network (cnn) model capable of handling multiple emotion labels per instance. their system integrates self-attention mechanisms to enhance sentence representation, allowing emotions to be classified independently within a single sentence. evaluated on semeval-2018 task 1, their approach ranked first in spanish and fifth in english, demonstrating its effectiveness across languages. ## experimental setup implementation. all experiments were conducted using a single rtx 4090 gpu with a batch size of 8, a linear learning rate schedule of 2e-5, and a weight decay of 0.01. after identifying the bestperforming strategy, we scaled up the approach and applied it to a larger model running on a single nvidia a100 gpu. all experiments were performed with 4-bit quantization and lora @xcite . model. we initially evaluated the qwen 2.5 7b @xcite , llama 3.1 8b @xcite , and phi-4 14b @xcite instruct models under few-shot and fine-tuning scenarios. based on its superior performance in brazilian portuguese, we selected qwen 2.5 7b as the base model for further experiments. the final tests were conducted using the qwen 2.5 70b instruct model @xcite , which shares pre-training and fine-tuning procedures similar to those of the selected base model. ## conclusion our study investigates how data quality and processing techniques influence emotion recognition in brazilian portuguese texts. we explored style-domain text translation, synthetic data generation, and the integration of a word-based emotion lexicon. our findings show that cross-lingual translation improves the balance of low-frequency emotion classes while preserving linguistic diversity, leading to better model generalization. additionally, our experiments demonstrate that training with emotion intensity labels, rather than binary labels, enhances performance when the outputs are binarized. model selection results suggest that smaller models can sometimes outperform larger ones in this task, emphasizing the importance of architecture choice in low-resource settings. finally, our post-competition refinements led to significant performance gains, reinforcing the role of fine-grained data processing strategies in improving emotion recognition models.