Milton R S


2024

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SSN_ARMM at SemEval-2024 Task 10: Emotion Detection in Multilingual Code-Mixed Conversations using LinearSVC and TF-IDF
Rohith Arumugam | Angel Deborah | Rajalakshmi Sivanaiah | Milton R S | Mirnalinee Thankanadar
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

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.

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TECHSSN at SemEval-2024 Task 10: LSTM-based Approach for Emotion Detection in Multilingual Code-Mixed Conversations
Ravindran V | Shreejith Babu G | Aashika Jetti | Rajalakshmi Sivanaiah | Angel Deborah | Mirnalinee Thankanadar | Milton R S
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

Emotion Recognition in Conversation (ERC) in the context of code-mixed Hindi-English interactions is a subtask addressed in SemEval-2024 as Task 10. We made our maiden attempt to solve the problem using natural language processing, machine learning and deep learning techniques, that perform well in properly assigning emotions to individual utterances from a predefined collection. The use of well-proven classifier such as Long Short Term Memory networks improve the model’s efficacy than the BERT and Glove based models. How-ever, difficulties develop in the subtle arena of emotion-flip reasoning in multi-party discussions, emphasizing the importance of specialized methodologies. Our findings shed light on the intricacies of emotion dynamics in code-mixed languages, pointing to potential areas for further research and refinement in multilingual understanding.