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| import pandas as pd |
| import numpy as np |
| import re |
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| _KNU_DICT = { |
| |
| "좋다":2,"훌륭하다":2,"뛰어나다":2,"만족":2,"감사":2,"행복":2,"기쁘다":2, |
| "즐겁다":2,"편리하다":2,"탁월하다":2,"우수하다":2,"친절":2,"빠르다":1, |
| "깨끗하다":1,"합리적":1,"저렴하다":1,"신뢰":2,"안전하다":1,"편하다":1, |
| "최고":2,"훌륭":2,"사랑":2,"좋아":2,"웃음":1,"밝다":1,"성공":2,"발전":1, |
| "유익":1,"효과적":1,"혁신":1,"창의":1,"희망":2,"긍정":2,"완벽":2,"탁월":2, |
| "추천":1,"칭찬":2,"흡족":2,"감동":2,"유용하다":1,"도움":1,"쉽다":1, |
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| "나쁘다":-2,"불만":-2,"싫다":-2,"실망":-2,"최악":-2,"불편":-1,"느리다":-1, |
| "비싸다":-1,"불안":-1,"위험":-1,"문제":-1,"불량":-2,"고장":-2,"오류":-2, |
| "짜증":-2,"화나다":-2,"슬프다":-2,"우울":-2,"부족":-1,"어렵다":-1, |
| "힘들다":-1,"복잡하다":-1,"불친절":-2,"무시":-2,"거짓":-2,"사기":-2, |
| "불신":-2,"두렵다":-1,"피곤":-1,"지루하다":-1,"부정":-2,"낭비":-1, |
| "손해":-2,"후회":-2,"걱정":-1,"아쉽다":-1,"부정적":-2,"억울":-2, |
| } |
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| _NEG_PATTERNS = re.compile(r"(안|못|없|않|안됨|불|비|무|미)") |
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| def _knu_score(text: str) -> float: |
| if not isinstance(text, str) or not text.strip(): |
| return 0.0 |
| tokens = re.findall(r'\w+', text) |
| score = 0.0 |
| count = 0 |
| for i, tok in enumerate(tokens): |
| for word, val in _KNU_DICT.items(): |
| if word in tok: |
| |
| neg = (i > 0 and _NEG_PATTERNS.search(tokens[i-1])) |
| score += -val if neg else val |
| count += 1 |
| return round(score / max(count, 1), 4) |
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| def _label(score: float, pos_thr: float = 0.3, neg_thr: float = -0.3) -> str: |
| if score >= pos_thr: return "긍정" |
| if score <= neg_thr: return "부정" |
| return "중립" |
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| |
| def _hf_sentiment(texts: list, model_name: str = "snunlp/KR-FinBert-SC"): |
| try: |
| from transformers import pipeline |
| pipe = pipeline("text-classification", model=model_name, |
| tokenizer=model_name, truncation=True, max_length=512) |
| results = [] |
| batch = 16 |
| for i in range(0, len(texts), batch): |
| chunk = [str(t)[:512] for t in texts[i:i+batch]] |
| out = pipe(chunk) |
| results.extend(out) |
| return results |
| except Exception as e: |
| return [{"error": str(e)}] * len(texts) |
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| |
| def run_sentiment(df: pd.DataFrame, text_col: str, |
| method: str = "dictionary", |
| model_name: str = "snunlp/KR-FinBert-SC", |
| pos_threshold: float = 0.3, |
| neg_threshold: float = -0.3): |
| """ |
| Parameters |
| ---------- |
| text_col : 텍스트 컬럼명 |
| method : 'dictionary' (KNU 사전, 빠름) | 'transformer' (딥러닝, 정확) |
| """ |
| texts = df[text_col].fillna("").tolist() |
|
|
| if method == "transformer": |
| try: |
| import transformers |
| except ImportError: |
| return None, "transformers 패키지가 설치되어 있지 않습니다. dictionary 방법을 사용하세요." |
| raw = _hf_sentiment(texts, model_name) |
| if "error" in raw[0]: |
| return None, f"Transformer 오류: {raw[0]['error']}" |
| labels = [r.get("label","").lower() for r in raw] |
| scores = [r.get("score", 0.0) for r in raw] |
| |
| def norm_label(l): |
| if "pos" in l or "긍정" in l: return "긍정" |
| if "neg" in l or "부정" in l: return "부정" |
| return "중립" |
| df_out = df[[text_col]].copy() |
| df_out["감정레이블"] = [norm_label(l) for l in labels] |
| df_out["신뢰도"] = [round(s, 4) for s in scores] |
| else: |
| scores = [_knu_score(t) for t in texts] |
| labels = [_label(s, pos_threshold, neg_threshold) for s in scores] |
| df_out = df[[text_col]].copy() |
| df_out["감정점수"] = scores |
| df_out["감정레이블"] = labels |
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| |
| label_counts = df_out["감정레이블"].value_counts() |
| total = len(df_out) |
| summary = pd.DataFrame({ |
| "감정": label_counts.index, |
| "빈도": label_counts.values, |
| "비율(%)": (label_counts.values / total * 100).round(1) |
| }) |
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| |
| if "감정점수" in df_out.columns: |
| pos_top = df_out.nlargest(5, "감정점수")[[text_col,"감정점수","감정레이블"]] |
| neg_top = df_out.nsmallest(5, "감정점수")[[text_col,"감정점수","감정레이블"]] |
| else: |
| pos_top = df_out[df_out["감정레이블"]=="긍정"].head(5) |
| neg_top = df_out[df_out["감정레이블"]=="부정"].head(5) |
|
|
| return { |
| "전체결과": df_out, |
| "요약": summary, |
| "긍정상위": pos_top.reset_index(drop=True), |
| "부정상위": neg_top.reset_index(drop=True) |
| }, None |
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