SSN _ ARMM at S em E val-2024 Task 10: Emotion Detection in Multilingual Code-Mixed Conversations using L inear SVC and TF - IDF (2024)

Abstract: Our paper explores a task involving the analysis of emotions and triggers within dialogues. We annotate each utterance with an emotion and identify triggers, focusing on binary labeling. We emphasize clear guidelines for replicability and conduct thorough analyses, including multiple system runs and experiments to highlight effective techniques. By simplifying the complexities and detailing clear methodologies, our study contributes to advancing emotion analysis and trigger identification within dialogue systems.

## introduction emotion recognition and trigger detection in conversational data represent critical frontiers in natural language processing (nlp) research, offering profound insights into human-computer interaction, sentiment analysis, and dialogue understanding. in today's interconnected world, where communication transcends linguistic boundaries, understanding the subtle nuances of emotions expressed in code-mixed dialogues becomes increasingly imperative. code-mixing, characterized by the seamless integration of multiple languages within a single conversation, reflects the rich tapestry of multicultural societies and presents unique challenges and opportunities for computational linguistics. additionally, in monolingual english dialogues, identifying triggers-key points where emotional shifts occur-serves as a gateway to unraveling the underlying sentiment dynamics and contextual flow of conversations. ## related work emotion recognition and trigger detection in conversational data have been subjects of active research in natural language processing (nlp) and affective computing. researchers have explored various approaches and methodologies to tackle these tasks, aiming to understand human emotions expressed in dialogue interactions and detect key points where emotional shifts occur. in this section, we review existing literature, highlighting recent advancements and key findings in the field. the paper "towards sub-word level compositions for sentiment analysis of hindi-english code mixed text" introduces a novel approach to sentiment analysis in code-mixed social media data. they present a hi-en code-mixed dataset and propose a subword-lstm architecture, enabling the model to capture sentiment information from important morphemes. this linguistic-driven approach outperforms traditional methods, achieving a notable accuracy improvement of 4-5% and surpassing existing systems by 18% in sentiment analysis of hi-en code-mixed text @xcite . advancements in sentiment analysis techniques now recognize the temporal variability of emotions in textual data. for example, the ssn mlrg1 team at semeval-2017 task 4 introduced a novel approach using the gaussian process with fixed rule multi-kernel learning for sentiment analysis of tweets. their method effectively captures evolving emotions by considering properties such as smoothness and periodicity. this approach aligns with our exploration of emotion recognition and trigger detection in conversational data, emphasizing the importance of incorporating temporal dynamics into sentiment analysis frameworks. @xcite this team also participated in task 5, focusing on fine-grained sentiment analysis. their system utilizes multiple kernel gaussian processes to identify optimistic and pessimistic sentiments associated with companies and stocks. given that comments on the same entities can exhibit varying emotions over time, considering properties like smoothness and periodicity becomes crucial. their experiments highlight the effectiveness of the multiple kernel gaussian process in capturing diverse properties compared to a single kernel gaussian process. @xcite in summary, existing research in emotion recognition and trigger detection in conversational data has explored diverse methodologies, including deep learning, machine learning, rule-based approaches, and multimodal fusion techniques. recent advancements have demonstrated the potential of context-aware features, multimodal data integration, and hybrid models in improving accuracy and robustness in these tasks. however, challenges such as linguistic diversity, cultural nuances, and ambiguity in emotional expressions continue to pose significant obstacles, warranting further research and exploration in the field. ## task description the tasks encompass emotion recognition and trigger detection in conversational data, each assessing specific competencies related to understanding emotions and detecting emotional shifts in dialogues. @xcite al. (2024a) 4.1 task 1 (erc for code-mixed) objective: this task aims to evaluate the system's capability to recognize emotions in code-mixed dialogues, where multiple languages are used interchangeably. description: we were provided with a dataset consisting of code-mixed dialogues, where utterances contain a mix of languages. the task involves identifying the emotions expressed in each utterance accurately. emotions may include disgust, contempt, anger, neutral, joy, sadness, fear, and surprise. @xcite 4.2 task 2 (efr for code-mixed) objective: this task focuses on assessing the system's performance in detecting triggers that indicate emotional shifts in code-mixed dialogues. description: we were presented with codemixed dialogues where emotional shifts occur. the task involves detecting these triggers within the dialogues. triggers are specific instances or phrases that signal a change in the emotional tone of the conversation. @xcite 4.3 task 3 (efr for english) objective: similar to task 2, this task evaluates the system's ability to detect triggers indicating emotional shifts. however, the focus is on englishonly dialogues. description: we were provided with a dataset containing english-only dialogues. the task remains the same as task 2, requiring us to identify triggers that signify emotional shifts within the conversations. @xcite these tasks aim to assess the robustness and effectiveness of systems in understanding and interpreting emotional nuances within conversational data, particularly in code-mixed and english-only settings. ## experimental setup in this section, we provide a detailed overview of the experimental setup. refer to 1 for the detailed architecture diagram illustrating the entire process. ## conclusion the exploration of emotion recognition and trigger detection in conversational data presents significant implications for natural language processing research and human-computer interaction. our study, encompassing tasks focused on code-mixed dialogues and english-only conversations, sheds light on the challenges and opportunities inherent in understanding the nuanced expressions of human emotions. through our experimental endeavors, we have demonstrated the efficacy of machine learning models, particularly linear support vector classifier (linearsvc), in recognizing emotions and detecting triggers within dialogues. despite the complexities posed by linguistic diversity and cultural nuances, our systems have shown promising performance, especially in identifying triggers indicating emotional shifts. however, our journey does not end here. future work should delve deeper into understanding the underlying causes of misclassifications and explore innovative approaches to enhance model robustness and generalization. additionally, incorporating context-aware features and leveraging advanced deep learning architectures could further improve the accuracy and granularity of emotion analysis in conversational data. in conclusion, our study contributes to the advancement of emotion analysis and trigger detection within dialogue systems, paving the way for more nuanced and culturally sensitive humanmachine interactions in diverse linguistic contexts. as we continue to unravel the intricacies of human emotions through computational linguistics, we embark on a journey toward more empathetic and intuitive artificial intelligence systems.