S yntax M ind at S em E val-2025 Task 11: BERT Base Multi-label Emotion Detection Using Gated Recurrent Unit (2025)

Abstract: Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B.

## introduction emotions play a significant role in shaping human behavior, speech patterns, and body language. natural language processing (nlp) plays a crucial role in analyzing and extracting valuable information based on emotions. earlier research on sentiment and emotion analysis has primarily focused on single-label classification, where a piece of text is assigned just one emotion or sentiment category, like "happy" or "sad". however, human emotions are rarely that simple, people often experience and express multiple emotions at once. for example, a movie review might convey both excitement and disappointment, or a social media post might reflect anger and fear simultaneously. multi-label emotion classification addresses this complexity by allowing a system to identify and tag multiple emotions within the same text, providing a more accurate and nuanced understanding of human emotional expression. to address this challenge, we present our submission for semeval-2025 task 11: bridging the gap in text-based emotion detection @xcite which is based on "brighter: bridging the gap in human-annotated textual emotion recognition datasets for 28 languages" @xcite and "evaluating the capabilities of large language models for multi-label emotion understanding" @xcite . the task is divided into three tracks and we participated in track a: multi-label emotion detection, and track b: emotion intensity. while participating in the task, we observed that training the model presented several challenges, particularly with overfitting and data imbalance. specifically, when using pre-trained embeddings like glove @xcite , the model over-fitted quickly, likely due to its tendency to memorize the training data rather than generalize to unseen examples. to address the unbalanced dataset, we experimented with smote (synthetic minority oversampling technique) @xcite , but this approach did not yield favorable results, possibly because it introduced synthetic samples that failed to capture the true emotional context of the data. additionally, we attempted to augment our dataset with data from the semeval-2018 affect in tweets @xcite task to enrich the training set. however, this also did not improve performance. finally we used bert as feature extractor with two stacked layer of gated recurrent units(grus) to overcome unstable training and achieve better convergence on overall (per language) dataset. we participated in a total of 19 languages for track a and 9 languages for track b. ## related works in the past most of emotion or sentiment analysis related work heavily relied on machine learning based approaches @xcite ; @xcite . such work critically depended on hand-crafted features. @xcite , provides two different methodologies to work on sentiment analysis on financial data. they used both machine learning based approach and deep learning technique in their study. in latter one, they incorporated convolution neural network (cnn) @xcite and gru to predict sentiment of financial data. @xcite , at semeval-2018 task 1, proposed a bidirectional long short-term memory @xcite (bi-lstm) architecture equipped with a multi-layer self attention mechanism. they used a set of word2vec word embeddings that were enhanced by a set of word emotional attributes and trained on an extensive collection of 550 million twitter messages. @xcite , proposed models, based on bi-lstm with multiple attention layers, implemented n independent attention mechanisms for n emotion labels, where each attention mechanism learns information specific to its corresponding emotion label. the study also implemented transformer models with multiple attention (ma) layers, including xlnet-ma, distilbert-ma, and roberta-ma. multiple attention mechanisms were incorporated into the output of these transformer models, and the models were fine-tuned on the datasets. ## experimental setup preprocessing was minimal across the languages studied. for languages other than english, no preprocessing steps were applied. for english, only basic operations were performed, including lowercasing text and expanding contractions, to standardize the input data. to train the model, we utilized bcewithlogitsloss as the loss function for multi-label classification, consistent rank logits (coral) @xcite as the loss function for emotion intensity, employed the adamw (loshchilov and hutter, 2019) optimizer for efficient parameter updates, and implemented early stopping to prevent overfitting. ## conclusion in this paper, we demonstrate our proposed model (bert + gru) for tackling the multi-label emotion challenge. although our performance was not optimal, we intend to improve our model in the coming days. we also aspire to participate in track 3 (cross-lingual emotion detection) in the future.