J elly K at S em E val-2025 Task 11: R ussian Multi-label Emotion Detection with Pre-trained BERT -based Language Models (2025)

Abstract: This paper presents our approach for SemEval-2025 Task 11, we focus on on multi-label emotion detection in Russian text (track A). We preprocess the data by handling special characters, punctuation, and emotive expressions to improve feature-label relationships. To select the best model performance, we fine-tune various pre-trained language models specialized in Russian and evaluate them using K-FOLD Cross-Validation. Our results indicated that ruRoberta-large achieved the best Macro F1-score among tested models. Finally, our system achieved fifth place in the unofficial competition ranking.

## introduction emotion detection is a subfield of natural language processing (nlp) that involves identifying and classifying emotions expressed in text according to what most people are likely to perceive the speaker to be feeling. importantly, the task does not aim to determine the actual emotional state of the speaker or the emotions of other entities mentioned in the text. for example, a sentence like "i am really happy now" would commonly be interpreted as expressing happiness due to the presence of emotional cues. this task has broad applications in various domains, making it an essential component in modern ai systems. in customer service, emotion detection enables companies to analyze user feedback, detect dissatisfaction, and improve overall user experience @xcite . in the mental health domain, it can help identify signs of emotional distress such as depression or anxiety from user input, supporting early intervention and automated screening processes @xcite . given these applications, @xcite task 11 focuses on the detection of multi-label emotion from sentences in several languages, including russian, to model how the general public would interpret the emotional content of a speaker's statement @xcite . in this paper, we describe our approach for track a of the task, which includes comprehensive data preprocessing techniques, the use of a pre-trained russian language model, and a k-fold cross-validation strategy to identify potential model weaknesses and reduce overfitting. we also perform model selection to find the best-fitting architecture for our dataset. our final system achieved fourth place on the official leaderboard for the russian sub-task. the implementation of our system is available on github foot_0 . ## system overview in this section, we describe the system in detail. we first clean the data and generate correlations between text and sentiments through data pre-processing. then we use k-fold cross-validation to evaluate the model and analyze the mispredicted patterns. furthermore, we use the sigmoid function @xcite to make predictions with a single threshold for all labels. finally, we experiment with different models to select the best models. ## conclusion in this paper, we introduced a good system to classify the speaker's emotions for the semeval challenge 2025 task 11 track a. the key point of our system is to find the relationship between data and labels by pre-processing and testing different models to choose the model that fits the dataset. our system achieved a top five ranking for russian in the rankings. victor sanh, lysandre debut, julien chaumond, and thomas wolf. 2019. distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arxiv, abs/1910.01108. xinyang zhang, yury malkov, omar florez, serim park, brian mcwilliams, jiawei han, and ahmed el-kishky. 2022. twhin-bert: a socially-enriched pre-trained language model for multilingual tweet representations. arxiv preprint arxiv:2209.07562. dmitry zmitrovich, alexander abramov, andrey kalmykov, maria tikhonova, ekaterina taktasheva, danil astafurov, mark baushenko, artem snegirev, tatiana shavrina, sergey markov, vladislav mikhailov, and alena fenogenova. 2023. a family of pretrained transformer language models for russian.