T ech SSN 3 at S em E val-2025 Task 11: Multi-Label Emotion Detection Using Ensemble Transformer Models and Lexical Rules (2025)

Abstract: Transformer models, specifically BERT-Large Uncased, DeBERTa, and RoBERTa, are first employed to classify the dataset, with their hyperparameters being fine-tuned to identify the most effective configuration. These models leverage deep contextual embeddings to capture nuanced semantic and syntactic information, making them powerful for sentiment analysis. However, transformer-based models alone may not fully capture the structural aspects of sentiment-bearing sentences.To address this, part-of-speech (POS) tagging is incorporated using a Hidden Markov Model (HMM) to analyze sentence structure and identify the key words responsible for conveying sentiment. By isolating adjectives, adverbs, and verbs, the lexical sentiment of individual words is determined using a polarity-based scoring method. This lexical score, derived from sentiment lexicons like SentiWordNet, provides an additional layer of interpretability, particularly in cases where transformer models struggle with implicit sentiment cues or negation handling.A key innovation in this approach is the adaptive weighting mechanism used to combine the outputs of the transformer models and lexical scoring. Instead of assigning uniform importance to each method, a unique weight is assigned to each model for every emotion category, ensuring that the best-performing approach contributes more significantly to the final sentiment prediction. For instance, DeBERTa, which excels in contextual understanding, is given more weight for subtle emotions like sadness, whereas lexical scoring is emphasized for emotions heavily influenced by explicit adjectives, such as joy or anger. The weight allocation is determined empirically through performance evaluation on a validation set, ensuring an optimal balance between deep learning-based contextual understanding and rule-based sentiment assessment.Additionally, traditional machine learning models such as Support Vector Machines (SVMs), Decision Trees, and Random Forests are tested for comparative analysis. However, these models demonstrate inferior performance, struggling with capturing deep contextual semantics and handling nuanced expressions of sentiment, reinforcing the superiority of the hybrid transformer + lexical approach.This method not only enhances interpretability but also improves accuracy, particularly in cases where sentiment is influenced by structural elements, negations, or compound expressions. The combined framework ensures a more robust and adaptable sentiment analysis model, effectively balancing data-driven learning and linguistic insights.

## introduction emotion detection plays a vital role in natural language processing (nlp) applications such as sentiment analysis, mental health monitoring, and human-computer interaction. unlike traditional classification tasks that label text as positive, negative, or neutral, real-world scenarios require identifying specific emotions like anger, sadness, joy, surprise, and fear, which often overlap and are highly context-dependent. in this study, we fine-tune multiple transformer-based models, including deberta, roberta, and bert large uncased, carefully optimizing hyperparameters to enhance classification performance. to further strengthen predictions, we incorporate a rulebased lexical system that assigns sentiment scores using an emotional lexicon and refines outputs based on part-of-speech (pos) patterns, particularly noun-verb-adverb-adjective combinations. by combining deep learning architectures with linguistic knowledge, our approach improves both the robustness and interpretability of emotion classification models. ## related works the shift from statistical models to deep learning has significantly improved multi-label emotion classification @xcite . transformer architectures like bert enhance contextual understanding and label dependencies @xcite , while multi-modal approaches combining text, audio, and visual cues further boost performance @xcite . fusion techniques, such as integrating wav2vec 2.0 with bert, have also shown promise @xcite . contextaware models refine emotion detection by capturing nuanced sentiment shifts @xcite . linguistic features further aid classification. pos tagging improves sentiment polarity detection @xcite , while hybrid models combining rule-based and deep learning approaches enhance robustness @xcite . finetuned transformers like roberta and deberta achieve state-of-the-art results @xcite , with lexicon-based scoring further refining sentiment interpretation @xcite . these techniques collectively strengthen emotion classification frameworks. ## dataset description this dataset, derived from the brighter corpus @xcite , is designed for englishlanguage emotion classification. each sample consists of a unique identifier, a text string, and five binary-labeled emotion categories: anger, fear, joy, sadness, and surprise. some examples from the datasets are given below in in ## methodology we propose an ensemble approach for multi-label emotion detection that integrates several fine-tuned bert-based models, traditional classifiers, and a lexical rule-based module. data preprocessing: text is normalized by lowercasing, removing punctuation, and filtering stopwords. tokenization is performed using bert's wordpiece tokenizer. @xcite modeling: multiple bert variants (bert-base, bert-large, deberta, and roberta) @xcite are fine-tuned using different hyperparameters-such as learning rates, batch sizes, and train-test splits-to identify optimal configurations. in parallel, traditional classifiers (e.g., naive bayes, logistic regression and svm) are trained on vectorized representations to serve as baseline comparisons. lexical analysis and ensembling: a lexical module assigns sentiment scores based on an emotional lexicon @xcite and limited grammatical pattern analysis (e.g., noun+verb+adverb+adjective structures). the predictions from the bert models, traditional classifiers, and lexical component are then combined using a weighted averaging scheme, with emotionspecific weights to balance their contributions. ## accuracy metrics since each emotion category is treated as a binary classification problem (0 or 1), and the dataset exhibits class imbalance, we use macro f1-score as the primary ranking metric. this ensures that both the minority and majority classes contribute equally to the overall performance @xcite . additionally, we evaluate the model using: the macro f1-score is computed as: ## results the models produced continuous scores rather than direct class labels, requiring thresholds for classification. as seen from traditional machine learning models struggled with emotion classification. logistic regression and naïve bayes showed lower performance due to their simplistic assumptions, particularly for surprise and anger, where contextual understanding is crucial. svm performed slightly better due to its decision boundary optimization but still fell short of deep learning approaches. these results emphasize the need for transformer-based models in nuanced sentiment classification tasks. ## conclusion our study demonstrates that even the best transformer-based models exhibit varying levels of effectiveness depending on the emotion being classified. some emotions, such as joy, are easier to detect due to explicit lexical indicators, whereas others, like sadness, are more nuanced and contextdependent. this explains why different bert variants perform differently across emotions-some capture explicit sentiment cues well, while others excel at detecting subtler patterns @xcite . additionally, our integration of lexical features proved valuable in cases where transformers struggled, particularly in scenarios where sentiment words were strong indicators. although lexicalbased models alone lack contextual understanding, their inclusion as a normalized feature significantly boosted classification performance for certain emotion categories. our experiments also emphasized the role of hyperparameters in optimizing performance. weight balancing across different bert variants and lexical scores was crucial in achieving an optimal fusion model. the hyperparameters were fine-tuned through multiple iterations, ultimately selecting a distribution that maximized macro-f1 scores. finally, the evaluation on unseen test data from the codabench semeval competition validated the robustness of our approach. the fusion method consistently outperformed individual models, demonstrating the advantage of leveraging diverse sentiment detection techniques.