README.md

metadata

license: apache-2.0

Qwen3-8B-Korean-Sentiment

Overview

This repository contains a fine-tuned model for Korean Sentiment Analysis (한국어 감정 분석) using a Large Language Model (LLM), specifically designed for YouTube comments in Korean. The model classifies sentiments into Positive(긍정), Negative(부정), and Neutral(중립) categories, and is fine-tuned to detect not only direct emotions but also subtle features like irony (반어법) and sarcasm (풍자) common in Korean-language content.

Sentiment Classification:

• Positive (긍정)
• Negative (부정)
• Neutral (중립)

Quickstart

To quickly get started with the fine-tuned model, use the following Python code:

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

# Load the model and tokenizer
model = AutoPeftModelForCausalLM.from_pretrained("LLM-SocialMedia/Qwen3-8B-Korean-Sentiment").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

model.eval()

# Sample comment
comment = "이거 너무 좋아요!"

# Format the prompt
prompt = (
    "다음은 유튜브 댓글입니다. 댓글의 감정을 분류해 주세요.\n\n"
    f"댓글: {comment}\n\n"
    "반드시 다음 중 하나만 출력하세요: 긍정 / 부정 / 중립"
)

# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt")

# Generate prediction
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=512)

# Decode and print the output
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])

Train/Test Details

• Training Dataset: Fine-tuned on 3,857 labeled YouTube comments for sentiment classification.
• Testing Dataset: Evaluated on 1,130 labeled YouTube comments to assess the model's performance.

Results

The fine-tuned model's performance on the sentiment classification task is summarized below:

Metric	Positive (긍정)	Neutral (중립)	Negative (부정)
Precision	0.8981	0.3787	0.4971
Recall	0.7362	0.2880	0.7413
F1-Score	0.8092	0.3272	0.5951
Support	527	309	344

Accuracy: 62.03% (Based on 1180 samples)

You can find detailed results here.

Contact

For any inquiries or feedback, feel free to contact the team:

• Email: [email protected]

Team:

• Hanjun Jung
• Jinsoo Kim
• Junhyeok Choi
• Suil Lee
• Seongjae Kang