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base_model: |
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- google-bert/bert-base-uncased |
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# Model Card: **Emotion Detection Model** |
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## Model Overview |
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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: |
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- Happiness |
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- Sadness |
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- Anger |
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- Fear |
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- Surprise |
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- Disgust |
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- Love |
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- Excitement |
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- Anticipation |
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- Contentment |
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- Confusion |
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- Frustration |
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- Nostalgia |
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## Intended Use |
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- **Primary Use Case:** Emotion detection in textual data, particularly for analyzing media content like TV shows, movies, or social media. |
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- **Scope of Application:** Suitable for analyzing dialogue or conversational data to identify emotional trends, character development, or audience engagement. |
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## Dataset |
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### Source |
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- Dialogues from the TV show *Friends*. |
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### Labeling |
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- Dialogues were labeled with 2–3 emotions per text using OpenAI's ChatGPT API. |
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- Labels represent a rich variety of emotional states. |
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## Methodology |
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### Labeling Process |
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- ChatGPT API was used to classify text into multiple emotions. |
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- Outputs were reviewed for accuracy and adjusted to minimize misclassification. |
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### Model Architecture |
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- **Pre-trained Models Used:** BERT |
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- Fine-tuned on labeled dialogue data for emotion detection. |
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- Supports multi-label classification to capture multiple emotions simultaneously. |
