Free Sentiment Analyzer | Paste Text or Upload CSV | Custom dictionary and Historical Mode Available
Dual-mode sentiment (semantic USE or lexicon). Analyze the score for the text and get ready to paste the text for your professional use case. Add a company-specific dictionary, and new words merge into scoring & highlighting immediately.
Dept: Product / Ops · Front-end only
Model
USE provides semantic understanding (heavier). Lexicon is immediate and offline-friendly.
Batch analyze (CSV)
Custom dictionary (company words)
Stored locally
Add domain-specific tokens and numeric weights. Positive favors positive sentiment; negative favors negative sentiment.
Tip: add product/feature names and company terms (e.g., "onboarding", "trial", "billing") to surface domain-specific sentiment.
Analysis result
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Mode: Lexicon
Sentiment · —
Score: —
Highlighted tokens
Per-sentence breakdown
Score distribution
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Recent analyses
Best for businesses
Designed for teams that need quick, explainable sentiment across reviews, support tickets, chat, and social — with company-specific tuning.
Customer feedback
Aggregate reviews, surface recurring complaints, and prioritize fixes.
Support & ops
Monitor ticket sentiment, escalate negative threads, and coach reps.
Marketing & social
Track campaign sentiment on Twitter, Reddit, and comments to react faster.
Why dual-mode?
Use the fast lexicon mode for offline, instant checks and the USE semantic mode when you need context-aware scoring (sarcasm and context-sensitive phrasing can be better captured with semantic embeddings).
Quick features
- Analyze customer feedback, chat messages, and emails.
- Customize scoring with company-specific keywords and weights.
- Bulk analyze via CSV and export results.
- Save history locally, no account required.
Frequently asked questions
What’s the best use of sentiment analysis for businesses?
Sentiment analysis helps teams quantify customer opinion at scale — useful for product prioritization, support triage, brand monitoring, and measuring campaign impact.
How do I interpret the sentiment score?
Scores are normalized values; positive means favorable sentiment, negative means unfavorable, and a near-zero score is neutral. Use per-sentence breakdowns and token highlights to find root causes.
Which mode is best — USE (semantic) or Lexicon?
Lexicon is fast and deterministic (great for predictable domains). USE is context-aware and can better capture nuanced language — choose USE for higher accuracy when analyzing diverse or conversational text.
Can I bulk-analyze reviews with CSV?
Yes — upload a CSV and choose the text column. Results can be downloaded as a CSV with sentiment labels and scores.
Can the tool detect sarcasm?
Detecting sarcasm is challenging. USE semantic mode can sometimes capture contextual cues, but no automated method is perfect—use manual sampling to validate edge cases.
Is my data stored or shared?
By default, LimeNit stores custom dictionaries and history locally in your browser.
How can I integrate the sentiment analyzer into my workflow?
Use batch analyzer for bulk sentiment analysis. You can automate the sentiment analysis score recording to your own platform with automation tools.