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ghcvbn/
sentiment-with-genz-slang-words-false-negative

Name: sentiment-with-genz-slang-words-false-negative
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Gen-Z Slang Sentiment RoBERTa

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment designed to solve the "Literal Trap" in sentiment analysis.

While standard models often flag Gen-Z hyperbolic slang (e.g., "I'm dead", "this is insane") as negative due to their literal dictionary definitions, this model recognizes them as high-intensity positive expressions when used in a conversational context.

Model Description

Developed by: Adil
Model type: RoBERTa-base
Language(s): English
Finetuned from model: cardiffnlp/twitter-roberta-base-sentiment
Domain: Social Media DMs, Informal Chat, Gen-Z Slang

Intended Uses & Limitations

Primary Use Case

Ideal for lead qualification and sentiment tracking in informal DM environments (Instagram, TikTok, WhatsApp) where users speak naturally and use high-energy slang.

Out-of-Scope

This model is not intended for formal document analysis, legal texts, or clinical psychology sentiment where words like "insane" or "dead" retain their literal, serious meanings.

Training Data

The model was fine-tuned on a Contrastive Dataset of 5,000+ examples. The data features "Semantic Pairs" to teach the model nuance:

Slang Positive: "omg im dead 💀" -> Positive
Literal Negative: "the patient is dead" -> Negative
Slang Positive: "that's sick!!" -> Positive
Literal Negative: "I feel very sick" -> Negative

How to Get Started

You can use this model directly with the Hugging Face pipeline:

python

from transformers import pipeline

classifier = pipeline("sentiment-analysis", model="ghcvbn/sentiment-with-genz-slang-words-false-negative")

# Test a slang phrase
result = classifier("this is insane, im terrified and im obsessed")
print(result)
# Expected: [{'label': 'positive', 'score': 0.98}]