datasets:
- ElKulako/stocktwits-crypto
language:
- en
tags:
- cryptocurrency
- crypto
- BERT
- sentiment classification
- NLP
- bitcoin
- ethereum
- shib
- social media
- sentiment analysis
- cryptocurrency sentiment analysis
For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689
CryptoBERT
CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. (A research paper with more details will follow soon.)
Classification Training
The model was trained on the following labels: "Bearish" : 0, "Neutral": 1, "Bullish": 2
CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from ElKulako/stocktwits-crypto.
CryptoBERT was trained with a max sequence length of 128. Technically, it can handle sequences of up to 514 tokens, however, going beyond 128 is not recommended.
Classification Example
from transformers import TextClassificationPipeline, AutoModelForSequenceClassification, AutoTokenizer
model_name = "ElKulako/cryptobert"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels = 3)
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, max_length=64, truncation=True, padding = 'max_length')
# post_1 & post_3 = bullish, post_2 = bearish
post_1 = " see y'all tomorrow and can't wait to see ada in the morning, i wonder what price it is going to be at. 😎🐂🤠💯😴, bitcoin is looking good go for it and flash by that 45k. "
post_2 = " alright racers, it’s a race to the bottom! good luck today and remember there are no losers (minus those who invested in currency nobody really uses) take your marks... are you ready? go!!"
post_3 = " i'm never selling. the whole market can bottom out. i'll continue to hold this dumpster fire until the day i die if i need to."
df_posts = [post_1, post_2, post_3]
preds = pipe(df_posts)
print(preds)
[{'label': 'Bullish', 'score': 0.8734585642814636}, {'label': 'Bearish', 'score': 0.9889495372772217}, {'label': 'Bullish', 'score': 0.6595883965492249}]
Training Corpus
CryptoBERT was trained on 3.2M social media posts regarding various cryptocurrencies. Only non-duplicate posts of length above 4 words were considered. The following communities were used as sources for our corpora:
(1) StockTwits - 1.875M posts about the top 100 cryptos by trading volume. Posts were collected from the 1st of November 2021 to the 16th of June 2022. ElKulako/stocktwits-crypto
(2) Telegram - 664K posts from top 5 telegram groups: Binance, Bittrex, huobi global, Kucoin, OKEx. Data from 16.11.2020 to 30.01.2021. Courtesy of Anton.
(3) Reddit - 172K comments from various crypto investing threads, collected from May 2021 to May 2022
(4) Twitter - 496K posts with hashtags XBT, Bitcoin or BTC. Collected for May 2018. Courtesy of Paul.