Elizabeth Martinez


2024

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VerbaNexAI Lab at SemEval-2024 Task 10: Emotion recognition and reasoning in mixed-coded conversations based on an NRC VAD approach
Santiago Garcia | Elizabeth Martinez | Juan Cuadrado | Juan Martinez-santos | Edwin Puertas
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

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.

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VerbaNexAI Lab at SemEval-2024 Task 3: Deciphering emotional causality in conversations using multimodal analysis approach
Victor Pacheco | Elizabeth Martinez | Juan Cuadrado | Juan Carlos Martinez Santos | Edwin Puertas
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This study delineates our participation in the SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations, focusing on developing and applying an innovative methodology for emotion detection and cause analysis in conversational contexts. Leveraging logistic regression, we analyzed conversational utterances to identify emotions per utterance. Subsequently, we employed a dependency analysis pipeline, utilizing SpaCy to extract significant chunk features, including object, subject, adjectival modifiers, and adverbial clause modifiers. These features were analyzed within a graph-like framework, conceptualizing the dependency relationships as edges connecting emotional causes (tails) to their corresponding emotions (heads). Despite the novelty of our approach, the preliminary results were unexpectedly humbling, with a consistent score of 0.0 across all evaluated metrics. This paper presents our methodology, the challenges encountered, and an analysis of the potential factors contributing to these outcomes, offering insights into the complexities of emotion-cause analysis in multimodal conversational data.

2023

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UTB-NLP at SemEval-2023 Task 3: Weirdness, Lexical Features for Detecting Categorical Framings, and Persuasion in Online News
Juan Cuadrado | Elizabeth Martinez | Anderson Morillo | Daniel Peña | Kevin Sossa | Juan Martinez-Santos | Edwin Puertas
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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.