Deep at S em E val-2025 Task 11: A Multi-Stage Approach to Emotion Detection (2025)

Abstract: This paper presents a novel text-based emotion detection approach for low-resource languages in SemEval-2025 Task 11. We fine-tuned Google Gemma 2 using tailored data augmentation and Chain-of-Thought prompting. Our method, incorporating supervised fine-tuning and model ensembling, significantly improved multi-label emotion recognition, intensity prediction, and cross-lingual performance. Results show strong performance in diverse low-resource settings. Challenges remain in fine-grained sentiment analysis. Future work will explore advanced data augmentation and knowledge transfer methods. This research demonstrates the potential of large language models for inclusive emotion analysis.

## introduction text sentiment analysis, a cornerstone of natural language processing (nlp), aims to computationally identify and extract subjective information from text, such as opinions, emotions, and attitudes. over the years, this field has evolved significantly, driven by the need to understand human sentiment in various domains, including customer feedback, social networks, and product reviews @xcite . traditional methods, such as lexicon-based approaches and machine learning models, have laid the foundation for sentiment analysis @xcite . however, the advent of large language models (llms) has revolutionized this field, offering new capabilities and challenges. early sentiment analysis methods relied heavily on lexicon-based techniques, where predefined sentiment scores were assigned to words, and heuristic rules were applied to aggregate these scores for an overall sentiment judgment. while these methods are computationally efficient, they often struggle with complex linguistic phenomena such as sarcasm, negation, and context-dependent sentiment. machine learning models, like naive bayes, support vector machines (svm) , introduced a data-driven approach to sentiment analysis @xcite . these models were trained on labeled datasets to classify text into positive, negative, or neutral categories. however, their performance was limited by the need for extensive labeled data and their inability to capture deep semantic relationships within the text. large language models (llms) like bert @xcite and chatgpt @xcite have revolutionized sentiment analysis, particularly in high-resource languages such as english. this advancement is largely attributed to their ability to achieve superior contextual understanding and facilitate zero-shot and few-shot learning through fine-tuning @xcite and prompt-based methods @xcite . specifically: (1) zero-shot and few-shot learning: llms' pre-trained language understanding enables them to perform sentiment analysis with minimal or no labeled data @xcite . (2) cross-lingual transfer: multilingual pre-training @xcite allows llms to transfer knowledge from high-resource to low-resource languages. however, despite their success in high-resource scenarios, llms face significant challenges when applied to low-resource languages @xcite , particularly those in africa and southeast asia. these challenges include: (1) data scarcity: the limited availability of labeled data in low-resource languages hinders the training of traditional supervised learning methods @xcite . (2) linguistic diversity: the complex syntax and diverse dialects present in these languages complicate model understanding and generalization. (3) cultural differences: the variations in emotional expression across cultures necessitate cross-cultural understanding, which is often challenging for models trained primarily on high-resource language data @xcite . in analogous event extraction tasks, the classical pipeline approach is divided into two stages, event argument extraction and event relation extraction, to enhance end-to-end accuracy @xcite . consequently, for low-resource scenarios, we draw upon this concept and propose a two-stage augmented data-based sentiment extraction method. the main contributions are summarized as: 1. we introduce a two-stage cot-enhanced data pipeline, which generates interpretable and diverse english augmented data for low-resource languages to assist in model training. 2. we employ techniques such as supervised fine-tuning (sft), k-fold cross-validation, model ensembling, and specialized lora adaptations. across all three tasks, our approach achieves substantially higher performance compared to the baselines. ## brighter dataset understanding how emotions are expressed differently across languages is crucial for building inclusive digital tools. @xcite ; @xcite have developed brighter, a comprehensive dataset encompassing 28 languages. brighter consists of 28 distinct datasets , each tailored to a specific language, designed to capture the nuanced expressions of emotions in text. these datasets are derived from a variety of sources, including social media posts, speeches, literary works, and news articles, ensuring a diverse representation of language usage. for some languages, new datasets were created, while existing ones were enhanced with automatically translated or generated data. each text instance within brighter is multilabeled, indicating the presence of one or more of six core emotions, along with a neutral category. furthermore, each emotion label is accompanied by an intensity rating on a 4-point scale, providing a more granular understanding of emotional expression. analysis of brighter revealed that emotion recognition remains a significant challenge for large language models (llms), particularly for languages with limited resources. semeval task 11 contains three tasks for emotion analysis @xcite : task a focuses on multi-label emotion detection, classifying text snippets into six emotions (joy, sadness, fear, anger, surprise, disgust), with varying presence of the "disgust" label across languages. task b involves emotion intensity prediction, assigning ordinal intensity levels (0-3) to given text and emotion pairs. task c tackles cross-lingual emotion detection, requiring prediction of emotion labels in a target language using training data from a different language. ## system overview in this study, we propose a novel approach for text sentiment analysis using large language models through supervised fine-tuning (sft), chain-of-thought (cot) prompting @xcite , data augmentation and model ensemble techniques. our methodology integrates advanced llm capabilities with traditional sentiment analysis frameworks to achieve higher accuracy and robustness in sentiment classification tasks. ## experimental setup to maximize coverage for multilingual tasks, we selected the gemma 2 multilingual model @xcite as the base model for augmenting and training. we employed bfloat16 precision and adam optimizer was configured with beta values of 0.9 and 0.999, and an epsilon of 1e-8. to optimize the training process, we set the learning rate to 7e-6. the batch size was configured to 64, which allowed for efficient utilization of computational resources while maintaining reasonable training speed. to ensure robust evaluation and mitigate overfitting, we adopted a 5-fold cross-validation strategy. for both task a and task b, we trained the models using the complete set of original data along with the augmented data, and employed mergekit @xcite for linear model fusion. for task c, we continue to employ mixed data while training dedicated lora heads for each individual language. ## results our proposed work demonstrates significant improvements across three tasks, leveraging gemma's cross-lingual capabilities enhanced through chainof-thought reasoning, data augmentation, and model-ensembling strategies. for task b emotion intensity, which requires simultaneous prediction of both emotion categories and intensity levels, our method demonstrates superior performance in for task c, crosslingual multi-label classification ( as shown in tables ## limitations our cross-lingual sentiment detection approach showed strong performance in task 3, but its effectiveness was notably reduced in tasks 1 and 2. this disparity is likely due to the training process involving datasets with both coarse and fine-grained sentiment annotations. the inherent differences in annotation granularity introduced inconsistencies, hindering the model's ability to accurately capture the subtle nuances of sentiment in these tasks. furthermore, the model performance in certain low-resource scenarios, particularly with javanese, fell significantly below acceptable levels. this underscores the persistent challenge of adapting large language models to languages with limited available data. future research should prioritize strategies for data augmentation in low-resource settings, such as back-translation and synthetic data generation, as well as the integration of language-specific linguistic resources. additionally, exploring methods for efficient knowledge transfer and adaptation from high-resource to low-resource languages is crucial for bridging the performance gap. ## conclusion this study investigated text-based emotion detection in low-resource languages, utilizing the google gemma 2 large language model. the research employed data augmentation, chain-of-thought (cot) prompting, and model ensembling techniques. the proposed approach achieved substantial performance gains across multilingual emotion detection and emotion intensity prediction task, outperforming state-of-the-art baselines. further research is needed to explore more effective knowledge transfer methods from high-resource to lowresource languages. overall, this work highlights the potential of large language models to bridge the gap in textbased emotion detection, particularly through data augmentation for resource-scarce language families.