Abstract
In the era of digital communication, detecting emotions has become crucial for applications ranging from customer service to mental health assessment. This study examines emotion recognition in text through various machine learning techniques, from traditional machine learning techniques, to advanced large language models (LLMs) such as BERT, Falcon 7B, and Mistral 7B. The results reveal that the fine-tuned Mistral 7B model is the most precise, achieving an accuracy of 76%. Furthermore, Support Vector Machine attained an accuracy of 64%.
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