UTBNLP at S emeval-2025 Task 11: Predicting Emotion Intensity with BERT and VAD -Informed Attention. (2025)

Abstract: Emotion intensity prediction plays a crucial role in affective computing, allowing for a more precise understanding of how emotions are conveyed in text. This study proposes a system that estimates emotion intensity levels by integrating contextual language representations with numerical emotion-based features derived from Valence, Arousal, and Dominance (VAD). The methodology combines BERT embeddings, predefined VAD values per emotion, and machine learning techniques to enhance emotion detection, without relying on external lexicons. The system was evaluated on the SemEval-2025 Task 11 Track B dataset, predicting five emotions (anger, fear, joy, sadness, and surprise) on an ordinal scale.The results highlight the effectiveness of integrating contextual representations with predefined VAD values, enabling a more nuanced representation of emotional intensity. However, challenges arose in distinguishing intermediate intensity levels, affecting classification accuracy for certain emotions. Despite these limitations, the study provides insights into the strengths and weaknesses of combining deep learning with numerical emotion modeling, contributing to the development of more robust emotion prediction systems. Future research will explore advanced architectures and additional linguistic features to enhance model generalization across diverse textual domains.

## introduction emotions are essential in language and vary in intensity depending on the context. understanding which emotions we express in a conversation and why they occur is crucial for improving the quality of human interactions @xcite . given a text and a perceived emotion, the system must estimate its level within a predefined scale, considering the language and its variations, thereby enabling a precise analysis of affective language @xcite . this article presents a system to predict the intensity of a set of emotions in a text fragment. this analysis builds upon previous models that have contributed to the identification of emotions present in textual comments. some emotions are more fundamental in physiological and cognitive terms, allowing them to manifest at different intensity levels @xcite . their significance lies in enhancing the automatic understanding of emotions in artificial intelligence and affective language analysis. to address this task, we proposed a system that relies on contextual language representations and emotional features that capture language representations and emotion-specific vad values, quantifying valence, arousal, and dominance @xcite . participation in this task allowed for the evaluation of the system's performance in detecting the intensity of perceived emotions compared to other approaches. although the system's performance was below the average, it successfully captured relevant patterns in emotional intensity variation. we observed that integrating contextual representations with vad-based emotional features enabled an approximation to the objective despite limitations in classifying certain emotions. the primary challenges arose in differentiating intermediate intensity levels, which affected accuracy in some categories. despite these challenges, the experience provided valuable insights for improving the modeling of emotional intensity in future studies. the code developed in this study is available for researchers and developers to access, analyze the implemented approaches, and replicate the experiments. by sharing the code, this work promotes the reproducibility of results. it fosters continuous improvement in the detection of perceived emotion intensity. 1 text joy fear anger sadness surprise none of us has mentioned the incident since. 0 1 0 2 1 was seven and woke up early, so i went to the ba... 1 0 0 0 0 " my god " " what's wrong with my arm? ", 0 2 0 0 1 ## background emotion recognition in text is an area of growing interest due to the rise of digital communication and the inherent complexity of language in these environments. previous research has approached this challenge from various perspectives, achieving significant advances in the semantic and syntactic understanding of texts and improving language representation across multiple languages @xcite . additionally, authors highlighted the importance of context, sentiment consistency, and common-sense knowledge to accurately detect emotions in such texts @xcite . these approaches emphasize the need for sophisticated models to capture the emotional richness of diverse digital interactions. emotion recognition models have evolved considerably, shifting from rule-based approaches to more adaptive methods, allowing a more precise and flexible interpretation of emotional language @xcite . these advancements have enhanced the ability of models to capture linguistic nuances, recognize context, and handle variations in emotional expression, which is crucial for practical applications in various domains. in this work, we participated in track 2 of task 11, which focuses on detecting and predicting emotion intensity in english text. the task consisted of identifying the perceived emotion in a text fragment and assigning it an intensity level based on a predefined ordinal scale. this approach allows a single comment to contain multiple emotions, each with an intensity level, enriching data analysis and interpretation. the dataset used for this task consisted of 117 developing samples, 2768 training samples, and test validation samples, in which we analyzed five specific emotions: joy, sadness, fear, anger, and surprise. the ordinal scale assigns the following values: 0 for no emotion, 1 for low intensity, 2 for moderate intensity, and 3 for high intensity. for example, in the sentence "none of us has mentioned the incident since", the system assigned the follow-ing intensities: joy (0), fear (1), anger (0), sadness (2), and surprise (1), illustrating the data structure and the model's response generation, which has been explored in previous studies ## system overview analyzing and predicting emotions from text follows a structured approach based on multiple stages. the methodology begins with data loading and preprocessing, followed by text representation using embeddings, fine-tuning a bert-based model, predicting valence, arousal, and dominance (vad) values, and finally converting these values into discrete emotion intensities. we evaluated the model using metrics that compare predictions with reference values to measure its accuracy and reliability, as shown in figure 1 . in the preprocessing stage, textual data undergoes thorough cleaning and normalization to ensure a standardized representation. first, the dataset is loaded, ensuring its structure is consistent and suitable for the model. then, text normalization techniques are applied, including lowercase conversion, removal of special characters, punctuation, and non-alphabetic elements, and expansion of contractions to enhance semantic coherence. we also removed stopwords to optimize text representation. additionally, we computed vad values to represent the emotion labels. once the text is processed, it is tokenized and represented using a pre-trained language model. in this case, bert @xcite is employed to convert the text into token sequences and attention masks. these numerical representations capture semantic and contextual relationships within the text, enabling a deeper analysis of expressed emotions. the next step involves training the model. we fine-tuned a bert-based model for predicting continuous vad values, and we adapted a pretrained model specialized in language comprehension through a fine-tuning process. during this phase, the model learns to associate textual patterns with corresponding vad values, optimizing its weights via a gradient descent algorithm and using a regression loss function. we trained the model using standard hyperparameters to fine-tune bert in classification tasks. we used a learning rate 2e-5 and three epochs to ensure convergence without overfitting. we set a batch size of 8 to optimize memory usage when processing long sequences. we employed adamw as the optimizer. a learning rate scheduler with an initial warm-up phase and linear decay was also applied to improve training stability and convergence. we selected these values based on best practices for emotional intensity prediction. after training, the model generates vad predictions for new data. these predictions provide a continuous estimation of the emotion present in each sentence, offering insights into the emotional intensity of the text. however, we converted these vad values into discrete emotional intensities to facilitate result interpretation. we achieved this through a mapping mechanism based on euclidean distances. in this process, we compared the predicted vad values with predefined reference values for each emotion, and we assigned the closest intensity on a scale from 0 to 3. finally, we evaluated the model using performance metrics. we calculated the pearson correlation to measure the relationship between predicted and reference vad values, assessing the model's capability to correctly identify emotional intensities. the results of these metrics help identify strengths and limitations, guiding future improvements in the emotion prediction system. the proposed methodology integrates advanced natural language processing techniques with deep learning models to predict emotions in text. by leveraging bert for language representation and a conversion system for mapping vad values to discrete intensities, a robust and efficient model is developed for analyzing emotions in textual conversations. ## experimental setup we got three datasets: training, development, and testing, ensuring that the model is trained and evaluated on entirely independent data. we applied techniques such as normalization, contraction expansion, pattern removal, and stopword elimination during preprocessing. we used tools like spacy and the contractions library to clean and standardize the text before using berttokenizer. hyperparameter tuning, including adjustments to the learning rate and number of epochs, is performed on the training set. in contrast, we used the development set for it-erative validation and model selection. finally, the pearson correlation coefficient is calculated individually for each dimension of the vad space, which is then transformed into emotional intensities to assess the quality of the predictions. ## result a key finding in our analysis is that integrating vad values improved the detection of emotions with well-defined characteristics in valence, arousal, and dominance. however, for emotions with less distinct distributions in this space, such as fear and surprise, the model tended to predict values closer to neutrality, which may explain the lower scores for these categories. the difference in performance between emotions could be attributed to an imbalance in the distribution of the class within the training set, limiting the model's ability to learn appropriate representations for less frequent or more ambiguous emotions while the model demonstrates promising performance in predicting certain emotions, the results suggest that additional adjustments are needed to improve its generalization capability. techniques such as class balancing, loss function adjustments, or incorporating additional linguistic features-such as deep semantic analysis or multimodal models integrating supplementary signals-could be explored to address these limitations. additionally, evaluating the model on more diverse datasets would allow for assessing its robustness across domains and contexts. ## conclusions this study presented a system for predicting the intensity of perceived emotions in a text by inte-