2nd/10_Wiki/Topics/AI_and_ML/Sentiment-Analysis.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-sentiment-analysis	Sentiment Analysis	10_Wiki/Topics	verified	self	Opinion Mining Emotion Detection Polarity Classification	none	A	0.9	applied	nlp sentiment classification llm		2026-05-10	pending	language framework python transformers/vllm

Sentiment Analysis

매 한 줄

"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.

매 핵심

매 Approaches

• Lexicon-based: VADER, TextBlob — 매 fast, 매 nuance X.
• Classical ML: TF-IDF + LogReg / SVM — 매 baseline.
• Transformer fine-tune: RoBERTa, DeBERTa-v3, XLM-R — 매 SOTA classification.
• LLM zero/few-shot: Claude / GPT — 매 aspect, sarcasm, code-switch handle.
• Multimodal: text + audio (prosody) + visual (face) — call center, video.

매 Granularity

• Document: overall polarity.
• Sentence: per-sentence.
• Aspect-based (ABSA): aspect + opinion + polarity (e.g., "battery=positive, screen=negative").
• Emotion: 6+ class (Ekman) — joy, anger, fear, ...

매 응용

1. Social listening — brand, product mention monitoring.
2. Customer support — ticket triage, escalation.
3. Finance — news / earnings call sentiment 의 alpha signal.
4. Product feedback — review aspect mining.

💻 패턴

Transformer fine-tune (2026 stack)

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
import numpy as np
from sklearn.metrics import f1_score

ds = load_dataset("sst2")
tok = AutoTokenizer.from_pretrained("microsoft/deberta-v3-large")
model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v3-large", num_labels=2)

def preprocess(b):
    return tok(b["sentence"], truncation=True, max_length=256)

ds = ds.map(preprocess, batched=True)

def metrics(eval_pred):
    preds = np.argmax(eval_pred.predictions, axis=1)
    return {"f1": f1_score(eval_pred.label_ids, preds, average="macro")}

args = TrainingArguments(
    output_dir="./out", num_train_epochs=3, per_device_train_batch_size=16,
    learning_rate=2e-5, eval_strategy="epoch", bf16=True,
)
Trainer(model, args, train_dataset=ds["train"], eval_dataset=ds["validation"],
        tokenizer=tok, compute_metrics=metrics).train()

LLM zero-shot (Claude 4.7)

import anthropic
client = anthropic.Anthropic()

def classify(text: str) -> dict:
    msg = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=200,
        system="Classify sentiment as positive/negative/neutral and extract aspects. Return JSON: {\"sentiment\":..., \"confidence\":0-1, \"aspects\":[{\"aspect\":..., \"polarity\":...}]}",
        messages=[{"role": "user", "content": text}],
    )
    import json
    return json.loads(msg.content[0].text)

print(classify("Battery lasts forever but the screen is dim."))

Aspect-based (ABSA) 의 fine-tune

# 매 PyABSA / DeBERTa-v3-ABSA
from pyabsa import AspectPolarityClassification as APC

config = APC.APCConfigManager.get_apc_config_english()
config.model = APC.APCModelList.FAST_LSA_T_V2
config.pretrained_bert = "microsoft/deberta-v3-base"

trainer = APC.APCTrainer(config=config, dataset="Laptop14",
                         from_checkpoint="english", auto_device=True)
ckpt = trainer.load_trained_model()
ckpt.predict(text="Battery is great but screen is dim",
             aspect="battery", print_result=True)

VADER (lexicon baseline)

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
v = SentimentIntensityAnalyzer()
print(v.polarity_scores("This is amazingly good!"))
# {'neg': 0.0, 'neu': 0.376, 'pos': 0.624, 'compound': 0.7424}

Multimodal (text + audio)

# 매 text RoBERTa + audio Wav2Vec2 의 late fusion
import torch
from transformers import AutoModel

text_emb = text_model(text_inputs).last_hidden_state.mean(1)  # [B, 768]
audio_emb = audio_model(audio_inputs).last_hidden_state.mean(1)  # [B, 768]
fused = torch.cat([text_emb, audio_emb], dim=-1)
logits = fusion_head(fused)

vLLM batch inference (production)

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-3.3-8B-Instruct")
prompts = [f"Sentiment of: {t}\nReply only positive/negative/neutral." for t in texts]
sp = SamplingParams(temperature=0.0, max_tokens=10)
outs = llm.generate(prompts, sp)

Calibration (production)

# 매 LLM confidence 의 calibrate — temperature scaling
from sklearn.linear_model import LogisticRegression
calib = LogisticRegression()
calib.fit(val_logits.reshape(-1, 1), val_labels)
prod_prob = calib.predict_proba(test_logits.reshape(-1, 1))

매 결정 기준

상황	Approach
Real-time, simple	VADER / TextBlob
Domain-specific, large data	Fine-tune DeBERTa-v3
Few labels, complex	LLM few-shot
Aspect granularity	PyABSA / GPT structured output
Multimodal	Late fusion or LLaVA-style

기본값: DeBERTa-v3 fine-tune for prod, Claude/GPT few-shot for prototyping.

🔗 Graph

• 부모: NLP
• 변형: Emotion-Recognition
• Adjacent: Transformer_Architecture_and_LLM_Foundations

🤖 LLM 활용

언제: zero-shot, sarcasm / nuance, low-data domain, ABSA structured output. 언제 X: high-throughput batch (use fine-tuned encoder), strict latency (<10ms).

❌ 안티패턴

• Lexicon on noisy / sarcastic: 매 fail on "great, just great".
• No domain adaptation: 매 finance / medical 의 generic model 의 underperform.
• Single label: 매 mixed sentiment ("good X, bad Y") 의 lose.
• No calibration: 매 LLM confidence 의 raw use.

🧪 검증 / 중복

• Verified (HuggingFace, PyABSA, Anthropic docs).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — full content with classical → LLM patterns