This is a simplified guide to an AI model called emotion_text_classifier maintained by michelleli99. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
emotion_text_classifier is a fine-tuned version of DistilRoBERTa-base designed to classify emotions in text. The model was trained on dialogue transcripts from the Friends television show, making it particularly suited for analyzing emotions in conversational contexts like Netflix shows and movies. It predicts seven emotion categories based on Ekman's emotion framework: anger, disgust, fear, joy, neutrality, sadness, and surprise.
This model builds upon the foundation of emotion-english-distilroberta-base, which was trained across six diverse emotion datasets. By fine-tuning on Friends transcripts, this model captures the specific emotional language patterns found in dialogue, offering a specialized alternative to its parent model for entertainment-focused applications.
Model inputs and outputs
emotion_text_classifier accepts text input and returns emotion predictions with confidence scores. The model processes dialogue or text snippets and identifies the dominant emotion present in the passage, making it straightforward to integrate into applications that need emotion detection.
Inputs
- Text strings: Any English language text, particularly dialogue from shows or movies
Outputs
- Emotion label: One of seven categories (anger, disgust, fear, joy, neutral, sadness, surprise)
- Confidence score: A decimal value between 0 and 1 indicating how confident the model is in its prediction
Capabilities
The model classifies emotions from conversational text with strong performance on dialogue-based content. It handles informal language patterns typical of television scripts, including slang, contractions, and colloquialisms. The model performs sentiment analysis at the sentence or utterance level, making it practical for analyzing individual character lines or brief emotional statements.
What can I use it for?
Content creators can use this model to automatically tag emotional beats in scripts or transcripts. Streaming platforms could implement it to generate emotion-based metadata for recommendations. Market researchers analyzing customer feedback could classify sentiment across support conversations. Developers building chatbots or virtual assistants can leverage it to understand user emotional state and adjust responses accordingly. Educational platforms analyzing student discussions can track emotional engagement patterns in learning environments.
Things to try
Test the model on dialogue from different genres to see how well it generalizes beyond Friends. Feed it sarcastic statements to explore how the model handles emotional ambiguity, since sarcasm often masks true sentiment. Try single-word inputs like "wow" or "ugh" to understand how the model interprets minimal context. Experiment with longer paragraphs of dialogue to observe whether the model identifies the dominant emotion or becomes confused by mixed sentiments. Compare predictions on the same emotional content written in formal versus casual language to understand how linguistic style affects classification.