What is Sentiment Analysis?
Sentiment analysis (opinion mining) classifies polarity and sometimes emotion/aspects in text. Approaches range from lexicon/rule methods to ML/transformers, including aspect-based models that score sentiments for specific attributes (e.g., fit, durability).
How It Works (quick)
- Preprocess: Language detect, normalize, de-duplicate, strip PII.
- Model: Lexicon + rules, classical ML, or fine-tuned transformers; optional aspect extraction.
- Outputs: Polarity (±/0), confidence, and aspect scores; aggregate at SKU/category level.
- Use: Re-rankers, QA alerts, moderation, merchandising badges (“Highly rated for warmth”).
Why It Matters in E-commerce
- Trust & conversion: Summaries and badges reduce effort.
- Signal for ranking: Down-weight items with persistent negative aspects (e.g., “runs small”).
- Ops: Triage tickets; detect policy risks and vendor issues.
Best Practices
- Domain & locale tuning: Train on your review language; handle slang and sarcasm cautiously.
- Aspect granularity: Define aspects that map to filterable attributes.
- Bias & safety: Watch for demographic bias; avoid PII leakage; keep human review for edge cases.
- Freshness: Weight recent reviews higher; decay old data.
- Explainability: Store example snippets behind each score.
Challenges
- Irony/sarcasm, mixed sentiments, short texts, image-only reviews, and cross-language drift.
Examples
- Aggregate “too tight” → size guidance and fit warning chip on PDP.
- “Battery life great, camera poor” → positive Battery, negative Camera aspect badges.
Summary
Sentiment analysis converts messy opinions into actionable scores and aspect insights. Tune per domain/locale, surface summaries/badges, and use capped signals in ranking and operations.
