This model is trained to detect nuanced emotions in text data, specifically focusing on dialogues from the TV show Friends and additional curated online content. By leveraging advanced deep learning architectures such as BERT and GPT, the model performs multi-label classification to identify up to 2–3 emotions per input dialogue. The targeted emotions include:
Happiness
Sadness
Anger
Fear
Surprise
Disgust
Love
Excitement
Anticipation
Contentment
Confusion
Frustration
Nostalgia
Intended Use
Primary Use Case: Emotion detection in textual data, particularly for analyzing media content like TV shows, movies, or social media.
Scope of Application: Suitable for analyzing dialogue or conversational data to identify emotional trends, character development, or audience engagement.
Dataset
Source
Dialogues from the TV show Friends.
Labeling
Dialogues were labeled with 2–3 emotions per text using OpenAI's ChatGPT API.
Labels represent a rich variety of emotional states.
Methodology
Labeling Process
ChatGPT API was used to classify text into multiple emotions.
Outputs were reviewed for accuracy and adjusted to minimize misclassification.
Model Architecture
Pre-trained Models Used: BERT
Fine-tuned on labeled dialogue data for emotion detection.
Supports multi-label classification to capture multiple emotions simultaneously.
