mnguyn11/phobert-stock-sentiment-PTDLW

Vietnamese Stock Sentiment Analysis (PhoBERT)

Model Description

This model is a Vietnamese financial sentiment analysis model fine-tuned from vinai/phobert-base for classifying the sentiment of Vietnamese financial news and stock-related text.

The model predicts three sentiment classes:

• —NEG – Negative sentiment
• —NEU – Neutral sentiment
• —POS – Positive sentiment

It is designed to analyze Vietnamese financial news, stock market reports, and investment-related text.

Model Details

• —Developed by: Minh Nguyen
• —Model type: Sequence Classification
• —Base model: vinai/phobert-base
• —Language: Vietnamese
• —Task: Sentiment Analysis
• —Number of labels: 3

Intended Use

Direct Use

The model can be used to classify sentiment in:

• —Vietnamese financial news
• —Stock market analysis
• —Investment reports
• —Financial social media posts

Example applications:

• —Market sentiment monitoring
• —Financial news analysis
• —Algorithmic trading signals
• —Sentiment-based stock screening

Out-of-Scope Use

This model is not designed for:

• —General Vietnamese sentiment analysis outside finance
• —Non-Vietnamese languages
• —Legal or medical text
• —Critical decision-making systems without human oversight

How to Use

Install required libraries:

pip install transformers torch pyvi
#Example inference:

!git clone https://huggingface.co/mnguyn11/phobert-stock-sentiment-PTDLW


import sys
import os
# Add the directory containing 'predict.py' to the Python path
project_dir = "/content/phobert-stock-sentiment-PTDLW"
if project_dir not in sys.path:
    sys.path.append(project_dir)


from predict import predict
predict(" Trong bối cảnh giá chung cư nội đô tăng mạnh và duy trì ở mức cao, dòng tiền đang dần chuyển sang các dự án còn nhiều dư địa tăng trưởng.")

Training Data

The model was fine-tuned using a dataset of Vietnamese financial news and stock-related sentences.

Dataset characteristics:

Domain: Finance / Stock Market

Language: Vietnamese

Labels:

NEG

NEU

POS

Approximate dataset size: ~1,200 labeled samples

Training Hyperparameters

Base model: vinai/phobert-base

Task: Sequence Classification

Number of labels: 3

Learning rate: 2e-5

Epochs: 3

Batch size: 16

Max sequence length: 256

Evaluation

Metrics

Evaluation metrics include:

Accuracy

Precision

Recall

F1-score

Results Model Accuracy Base PhoBERT ~33% Fine-tuned Model ~90%

The fine-tuned model significantly outperforms the base PhoBERT model on the financial sentiment classification task.

Limitations

The dataset size is relatively small (~1k samples)

The model is domain-specific to financial text

Performance may degrade on informal or slang-heavy Vietnamese

Environmental Impact

Training was conducted on a consumer GPU environment for experimentation purposes.

Acknowledgements

This model is based on the Vietnamese language model:

PhoBERT developed by VinAI Research.