FinTwitBERT-wsb-sentiment

This model is a fine-tuned version of FinTwitBERT-sentiment, specifically adapted to understand the informal financial jargon, slang, and sarcasm used on retail trading subreddits like r/wallstreetbets.

Intended Uses

I finetuned this model so that it can also be used to analyze r/wallstreetbets posts. I noticed the slang and language usage differs compared to Twitter/X users.

Dataset

FinTwitBERT-wsb-sentiment has been finetuned on moonscape95/WSBS's dataset. Specifically on the WSB_all_agree.csv dataset.

Training Code

You can find the Python code here.

Usage

Using HuggingFace's transformers library the model and tokenizers can be converted into a pipeline for text classification.

from transformers import pipeline

# Create a sentiment analysis pipeline
pipe = pipeline(
    "sentiment-analysis",
    model="StephanAkkerman/FinTwitBERT-wsb-sentiment",
)

# Get the predicted sentiment
print(pipe("Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑"))

Labels

• 0: NEUTRAL
• 1: BULLISH
• 2: BEARISH