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

Abstract: This paper presents our system developed for the SemEval-2025 Task 11:Bridging the Gap in Text-Based Emotion Detection, on Track A: Multi-label Emotion Detection.Given a target text snippet, predict the perceived emotion(s) of the speaker. Specifically, select whether each of the following emotions apply: joy, sadness, fear, anger, surprise, or disgust. To this end, we focus on English source language selection strategies on four different pre-trained languages models: google-bert,FacebookAI-roberta,dccuchile-bert and distilbert-multi.We experiment with 1) the training set data is analyzed visually, 2) multiple numbers of single models are trained on the training set data, and 3) multiple number of single models for votingweight ensemble learning. We further study the influence of different hyperparameters on the integrated model and select the best integration model for the prediction of the test set. Our submission achieved the good ranking place in the test set.Emotion Macro F1 Score 0.6998 and Emotion Micro F1 Score 0.7374. For the final ranking, organizers will use the Macro F1 score.Even so, my approach has yielded good results.

## introduction emotions are simultaneously familiar and mysterious. @xcite on the one hand, we all express and manage our emotions every day. yet, on the other hand, emotions are complex, nuanced, and sometimes hard to articulate. we also use language in subtle and complex ways to express emotion.further, people are highly variable in how they perceive and express emotions (even within the same culture or social group).thus, we can never truly identify how one is feeling based on something that they have said with absolute certainty.emotion recognition is not one task but an umbrella term for several tasks such as detecting the emotions of the speaker, identifying what emotion a piece of text is conveying and detecting emotions evoked in a reader. based on the predictive task background of predictive emotion text, we propose an ensemble learning method based on pre-trained language model. the code of this method is available on my github website. foot_0 2 related work semeval in previous years has introduced tasks focusing on multi-label text classification and text binary classification @xcite @xcite to evaluate internal potential elements and potential content of the text.these tasks provided datasets with human labeled similarity scores, which have been extensively utilized for training sentence embedding models and conducting semantic evaluations. ## conclusion our system employs an ensemble approach to estimate semantic relatedness(eneko agirre and wiebe, 2014),integrating results from multiple systems:google-bert-base-multilingual-uncased and facebookai-roberta-base.the hyperparameter is following: eval-batch-size is 8,num-train-epochs is 5,learning-rate is 4e-05,optimizer is adamw,useearly-stopping is true.the dataset usage is shown in our experiments are based on english language data sets only. constrained by the size of the training data and the availability of pre-trained language models, it is regrettable that we did not offer insights into other asian and african languages.in future research,studies on low-resource languages will be valuable, including tasks such as data collection, annotation,and pre-training models tailored to these languages.