T ele AI at S em E val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection with Prompt Engineering and Data Augmentation (2025)

Abstract: This paper presents the approach we employed in SemEval-2025 Task 11: “Bridging the Gap in Text-Based Emotion Detection.” The core objective of this shared task is emotion perception, focusing on determining the emotion the speaker is likely expressing when uttering a sentence or short text fragment, as perceived by the majority. In this task, we applied a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model’s performance. Through these optimizations, the model demonstrated improved accuracy and stability in emotion detection. Ultimately, in both Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity Prediction), our approach achieved top-3 rankings across multiple languages, showcasing the effectiveness and cross-lingual adaptability of our method.

## introduction emotion recognition is one of the core tasks in the field of natural language processing (nlp), aiming to identify and understand human emotional states from texts, dialogues, and other forms of data. with the rapid growth of data sources such as social media, online reviews, and customer feedback, sentiment analysis has become an indispensable tool across various industries, particularly in fields such as marketing, brand monitoring, public opinion analysis, and mental health @xcite . despite significant progress in sentiment classification and prediction tasks @xcite @xcite , the subjective and complex nature of emotions makes emotional expression more challenging due to factors such as individual differences, cultural background, and context. for instance, people may have vastly different emotional reactions to the same event, necessitating that sentiment recognition systems possess enhanced adaptability and flexibility to handle the complex and varied expressions of emotions across diverse contexts. to address these challenges and bridge existing gaps, semeval-2025 task 11: bridging the gap in text-based emotion detection introduces a largescale emotion recognition dataset covering multiple languages @xcite , aimed at advancing emotion detection technologies. this task consists of three sub-tasks: track a, @xcite @xcite . it presents new challenges and opportunities for researchers in the field of emotion recognition, particularly in handling cross-lingual and multi-label sentiment tasks. in this paper, we employed a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model's performance. specifically, we dynamically adjusted the prompt designs to help the model better understand and capture the subtle nuances of emotional expressions. the data augmentation techniques expanded the training set by generating synthetic data, particularly for categories with fewer emotion intensity samples, effectively addressing the data imbalance issue. furthermore, the ensemble voting strategy, which combining predictions from multiple models, further improved the accuracy and stability of emotion detection. during the testing phase, we selected the optimal model combination based on the results from the validation set for submission. our approach achieved second place for chinese in track a, second place for chinese, and third place for english in track b. ## experiment in our experiments, we selected qwen2.5-72b-instruct @xcite as the base model and fine-tuned it using lora methodology. the batch size was set to 32, the learning rate was set to 1.0e-4, and the model was trained for a total of 5 epochs. ## conclusion in this study, we have proposed an effective approach for emotion intensity prediction and multilabel emotion detection. by leveraging techniques such as carefully designed prompts, data augmentation through llm-generated synthetic data, and dynamic optimization, we significantly improved model performance. the introduction of ensemble voting further stabilized and enhanced the model's classification accuracy. the experimental results on both track a and track b validate the effectiveness of our method, demonstrating its strong performance in both english and chinese, and its generalizability to other languages. future work could focus on extending the application to more languages, refining the model's ability to handle nuanced emotional expressions, and improving the scalability of the data augmentation strategies.