Ensuring food safety requires effective detection of potential hazards in food products. This paper presents the participation of VerbaNexAI in the SemEval-2025 Task 9 challenge, which focuses on the automatic identification and classification of food hazards from descriptive texts. Our approach employs a machine learning-based strategy, leveraging a Random Forest classifier combined with TF-IDF vectorization and character n-grams (n=2-5) to enhance linguistic pattern recognition. The system achieved competitive performance in hazard and product classification tasks, obtaining notable macro and micro F1 scores. However, we identified challenges such as handling underrepresented categories and improving generalization in multilingual contexts. Our findings highlight the need to refine preprocessing techniques and model architectures to enhance food hazard detection. We made the source code publicly available to encourage reproducibility and collaboration in future research.

Emotion detection in text has become a highly relevant research area due to the growing interest in understanding emotional states from human interaction in the digital world. This study presents an approach for emotion detection in text using a RoBERTa-based model, optimized for multi-label classification of the emotions joy, sadness, fear, anger, and surprise in the context of the SemEval 2025 - Task 11: Bridging the Gap in Text-Based Emotion Detection competition. Advanced preprocessing strategies were incorporated, including the augmentation of the training dataset through automatic translation to improve the representativeness of less frequent emotions. Additionally, a loss function adjustment mechanism was implemented to mitigate class imbalance, enabling the model to enhance its detection capability for underrepresented categories. The experimental results reflect competitive performance, with a macro F1 of 0.6577 on the development set and 0.6266 on the test set. In the competition, the model ranked 47th, demonstrating solid performance against the challenge posed.

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.

This study introduces an innovative approach to emotion recognition and reasoning about emotional shifts in code-mixed conversations, leveraging the NRC VAD Lexicon and computational models such as Transformer and GRU. Our methodology systematically identifies and categorizes emotional triggers, employing Emotion Flip Reasoning (EFR) and Emotion Recognition in Conversation (ERC). Through experiments with the MELD and MaSaC datasets, we demonstrate the model’s precision in accurately identifying emotional shift triggers and classifying emotions, evidenced by a significant improvement in accuracy as shown by an increase in the F1 score when including VAD analysis. These results underscore the importance of incorporating complex emotional dimensions into conversation analysis, paving new pathways for understanding emotional dynamics in code-mixed texts.

Nowadays, persuasive messages are more and more frequent in social networks, which generates great concern in several communities, given that persuasion seeks to guide others towards the adoption of ideas, attitudes or actions that they consider to be beneficial to themselves. The efficient detection of news genre categories, detection of framing and detection of persuasion techniques requires several scientific disciplines, such as computational linguistics and sociology. Here we illustrate how we use lexical features given a news article, determine whether it is an opinion piece, aims to report factual news, or is satire. This paper presents a novel strategy for news based on Lexical Weirdness. The results are part of our participation in subtasks 1 and 2 in SemEval 2023 Task 3.