Sentiment Inferencing model for stock related commments
A project by NUS ISS students Frank Cao, Gerong Zhang, Jiaqi Yao, Sikai Ni, Yunduo Zhang
Description
This model is fine tuned with roberta-base model on 3200000 comments from stocktwits, with the user labeled tags 'Bullish' or 'Bearish'
try something that the individual investors may say on the investment forum on the inference API, for example, try 'red' and 'green'.
Training information
- batch size 32
- learning rate 2e-5
| Train loss | Validation loss | Validation accuracy | |
|---|---|---|---|
| epoch1 | 0.3495 | 0.2956 | 0.8679 |
| epoch2 | 0.2717 | 0.2235 | 0.9021 |
| epoch3 | 0.2360 | 0.1875 | 0.9210 |
| epoch4 | 0.2106 | 0.1603 | 0.9343 |
How to use
from transformers import RobertaForSequenceClassification, RobertaTokenizer
from transformers import pipeline
import pandas as pd
import emoji
# the model was trained upon below preprocessing
def process_text(texts):
# remove URLs
texts = re.sub(r'https?://\S+', "", texts)
texts = re.sub(r'www.\S+', "", texts)
# remove '
texts = texts.replace(''', "'")
# remove symbol names
texts = re.sub(r'(\#)(\S+)', r'hashtag_\2', texts)
texts = re.sub(r'(\$)([A-Za-z]+)', r'cashtag_\2', texts)
# remove usernames
texts = re.sub(r'(\@)(\S+)', r'mention_\2', texts)
# demojize
texts = emoji.demojize(texts, delimiters=("", " "))
return texts.strip()
tokenizer_loaded = RobertaTokenizer.from_pretrained('zhayunduo/roberta-base-stocktwits-finetuned')
model_loaded = RobertaForSequenceClassification.from_pretrained('zhayunduo/roberta-base-stocktwits-finetuned')
nlp = pipeline("text-classification", model=model_loaded, tokenizer=tokenizer_loaded)
sentences = pd.Series(['just buy','just sell it',
'entity rocket to the sky!',
'go down','even though it is going up, I still think it will not keep this trend in the near future'])
# sentences = list(sentences.apply(process_text)) # if input text contains https, @ or # or $ symbols, better apply preprocess to get a more accurate result
sentences = list(sentences)
results = nlp(sentences)
print(results) # 2 labels, label 0 is bearish, label 1 is bullish