RoBERTa Steam Review Emotions
This is a fine-tuned roberta-base model trained specifically to detect 28 distinct emotional states within the context of Steam video game reviews.
Model Details
- Base Model:
roberta-base - Task: Text Classification (Multi-class / 28 Labels)
- Language: English
- Training Dataset:
bramb0/synthetic-steam-emotions
Intended Use
This model is designed to perform highly nuanced emotion detection on gaming-related text, such as Steam reviews, Reddit threads, and Discord messages. It goes beyond standard Positive/Negative sentiment by predicting the probabilities of 28 distinct GoEmotion labels.
Labels
The model predicts the following 28 emotions:
admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise, neutral.
Training Data
The model was trained on a custom dataset of Steam Reviews. The reviews were split into individual clauses and tagged with one of the 28 emotion labels using an LLM to generate synthetic ground-truth data.
Usage
from transformers import pipeline
classifier = pipeline("text-classification", model="bramb0/roberta-steam-review-emotions", return_all_scores=True)
results = classifier("The gameplay is a bit mid but overall it's a 7/10 game, it has good story and lore!")
print(results)