refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-sentiment-analysis
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title: Sentiment Analysis
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Opinion Mining, Emotion Detection, Polarity Classification]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [nlp, sentiment, classification, llm]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: transformers/vllm
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---
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# Sentiment Analysis
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## 매 한 줄
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> **"매 sentiment analysis 의 lexicon → ML → transformer → LLM 의 evolution"**. 매 2026 의 SOTA 의 fine-tuned RoBERTa / DeBERTa-v3 (90%+ F1 on SST-5) + LLM zero-shot (Claude Opus 4.7, GPT-5) 의 nuance / aspect / sarcasm 의 handle. 매 multimodal (text + voice + face) 의 production 의 standard.
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## 매 핵심
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### 매 Approaches
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- **Lexicon-based**: VADER, TextBlob — 매 fast, 매 nuance X.
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- **Classical ML**: TF-IDF + LogReg / SVM — 매 baseline.
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- **Transformer fine-tune**: RoBERTa, DeBERTa-v3, XLM-R — 매 SOTA classification.
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- **LLM zero/few-shot**: Claude / GPT — 매 aspect, sarcasm, code-switch handle.
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- **Multimodal**: text + audio (prosody) + visual (face) — call center, video.
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### 매 Granularity
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- **Document**: overall polarity.
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- **Sentence**: per-sentence.
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- **Aspect-based (ABSA)**: aspect + opinion + polarity (e.g., "battery=positive, screen=negative").
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- **Emotion**: 6+ class (Ekman) — joy, anger, fear, ...
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### 매 응용
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1. **Social listening** — brand, product mention monitoring.
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2. **Customer support** — ticket triage, escalation.
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3. **Finance** — news / earnings call sentiment 의 alpha signal.
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4. **Product feedback** — review aspect mining.
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## 💻 패턴
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### Transformer fine-tune (2026 stack)
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```python
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
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from datasets import load_dataset
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import numpy as np
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from sklearn.metrics import f1_score
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ds = load_dataset("sst2")
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tok = AutoTokenizer.from_pretrained("microsoft/deberta-v3-large")
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model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v3-large", num_labels=2)
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def preprocess(b):
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return tok(b["sentence"], truncation=True, max_length=256)
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ds = ds.map(preprocess, batched=True)
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def metrics(eval_pred):
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preds = np.argmax(eval_pred.predictions, axis=1)
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return {"f1": f1_score(eval_pred.label_ids, preds, average="macro")}
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args = TrainingArguments(
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output_dir="./out", num_train_epochs=3, per_device_train_batch_size=16,
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learning_rate=2e-5, eval_strategy="epoch", bf16=True,
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)
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Trainer(model, args, train_dataset=ds["train"], eval_dataset=ds["validation"],
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tokenizer=tok, compute_metrics=metrics).train()
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```
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### LLM zero-shot (Claude 4.7)
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```python
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import anthropic
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client = anthropic.Anthropic()
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def classify(text: str) -> dict:
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msg = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=200,
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system="Classify sentiment as positive/negative/neutral and extract aspects. Return JSON: {\"sentiment\":..., \"confidence\":0-1, \"aspects\":[{\"aspect\":..., \"polarity\":...}]}",
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messages=[{"role": "user", "content": text}],
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)
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import json
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return json.loads(msg.content[0].text)
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print(classify("Battery lasts forever but the screen is dim."))
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```
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### Aspect-based (ABSA) 의 fine-tune
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```python
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# 매 PyABSA / DeBERTa-v3-ABSA
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from pyabsa import AspectPolarityClassification as APC
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config = APC.APCConfigManager.get_apc_config_english()
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config.model = APC.APCModelList.FAST_LSA_T_V2
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config.pretrained_bert = "microsoft/deberta-v3-base"
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trainer = APC.APCTrainer(config=config, dataset="Laptop14",
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from_checkpoint="english", auto_device=True)
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ckpt = trainer.load_trained_model()
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ckpt.predict(text="Battery is great but screen is dim",
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aspect="battery", print_result=True)
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```
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### VADER (lexicon baseline)
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```python
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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v = SentimentIntensityAnalyzer()
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print(v.polarity_scores("This is amazingly good!"))
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# {'neg': 0.0, 'neu': 0.376, 'pos': 0.624, 'compound': 0.7424}
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```
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### Multimodal (text + audio)
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```python
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# 매 text RoBERTa + audio Wav2Vec2 의 late fusion
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import torch
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from transformers import AutoModel
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text_emb = text_model(text_inputs).last_hidden_state.mean(1) # [B, 768]
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audio_emb = audio_model(audio_inputs).last_hidden_state.mean(1) # [B, 768]
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fused = torch.cat([text_emb, audio_emb], dim=-1)
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logits = fusion_head(fused)
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```
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### vLLM batch inference (production)
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="meta-llama/Llama-3.3-8B-Instruct")
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prompts = [f"Sentiment of: {t}\nReply only positive/negative/neutral." for t in texts]
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sp = SamplingParams(temperature=0.0, max_tokens=10)
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outs = llm.generate(prompts, sp)
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```
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### Calibration (production)
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```python
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# 매 LLM confidence 의 calibrate — temperature scaling
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from sklearn.linear_model import LogisticRegression
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calib = LogisticRegression()
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calib.fit(val_logits.reshape(-1, 1), val_labels)
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prod_prob = calib.predict_proba(test_logits.reshape(-1, 1))
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Real-time, simple | VADER / TextBlob |
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| Domain-specific, large data | Fine-tune DeBERTa-v3 |
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| Few labels, complex | LLM few-shot |
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| Aspect granularity | PyABSA / GPT structured output |
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| Multimodal | Late fusion or LLaVA-style |
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**기본값**: DeBERTa-v3 fine-tune for prod, Claude/GPT few-shot for prototyping.
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## 🔗 Graph
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- 부모: [[NLP]]
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- 변형: [[Emotion-Recognition]]
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- Adjacent: [[Transformer_Architecture_and_LLM_Foundations|Transformers]]
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## 🤖 LLM 활용
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**언제**: zero-shot, sarcasm / nuance, low-data domain, ABSA structured output.
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**언제 X**: high-throughput batch (use fine-tuned encoder), strict latency (<10ms).
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## ❌ 안티패턴
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- **Lexicon on noisy / sarcastic**: 매 fail on "great, just great".
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- **No domain adaptation**: 매 finance / medical 의 generic model 의 underperform.
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- **Single label**: 매 mixed sentiment ("good X, bad Y") 의 lose.
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- **No calibration**: 매 LLM confidence 의 raw use.
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## 🧪 검증 / 중복
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- Verified (HuggingFace, PyABSA, Anthropic docs).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full content with classical → LLM patterns |
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