Abstract
The research focuses on emotion detection in multilingual conversations, particularly in Romanized Hindi and English, with applications in sentiment analysis and mental health assessments. The study employs Machine learning, deep learning techniques, including Transformer-based models like XLM-RoBERTa, for feature extraction and emotion classification. Various experiments are conducted to evaluate model performance, including fine-tuning, data augmentation, and addressing dataset imbalances. The findings highlight challenges and opportunities in emotion detection across languages and emphasize culturally sensitive approaches. The study contributes to advancing emotion analysis in multilingual contexts and provides practical guidance for developing more accurate emotion detection systems.- Anthology ID:
- 2024.semeval-1.177
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1217–1221
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.177
- DOI:
- Cite (ACL):
- Monika Vyas. 2024. MorphingMinds at SemEval-2024 Task 10: Emotion Recognition in Conversation in Hindi-English Code-Mixed Conversations. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1217–1221, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- MorphingMinds at SemEval-2024 Task 10: Emotion Recognition in Conversation in Hindi-English Code-Mixed Conversations (Vyas, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.177.pdf