Sentiment Analyzer
Analyze the emotional tone of any text with lexicon-based sentiment scoring. Detects positive, negative, and neutral sentiment with per-sentence breakdown — fully client-side.
How to Use
Analyze the sentiment of any text in three steps:
- Paste your text — Enter a product review, social media post, article, email, or any English text. Click a sample button to try a pre-loaded example.
- Click Analyze — The tool scores every word against the AFINN-165 lexicon (2,477 words rated from -5 to +5). It detects negation ("not good" flips polarity) and intensifiers ("very good" amplifies the score).
- Review results — See the overall sentiment gauge, per-sentence breakdown with matched words highlighted, and statistics on positive vs. negative word counts. Copy the summary with the clipboard button.
About This Tool
AFINN-165 Lexicon
AFINN is a sentiment word list developed by Finn Årup Nielsen at the Technical University of Denmark. Version 165, published in 2011 and updated through 2015, contains 2,477 English words and phrases manually rated on an integer scale from -5 (most negative) to +5 (most positive). Each rating reflects the word's typical emotional valence across contexts. The lexicon is widely cited in academic research and production NLP systems for its balance of simplicity and coverage.
Unlike machine learning approaches that require training data and GPU compute, lexicon-based analysis is deterministic — the same input always produces the same output. This makes it transparent, debuggable, and fast enough to run in a browser with zero latency. The trade-off is reduced accuracy on sarcasm, irony, domain-specific jargon, and context-dependent meaning.
Scoring Algorithm
The analyzer tokenizes text into individual words and looks up each token in the AFINN lexicon. Two modifier systems adjust raw scores: negation detection identifies words like "not", "never", and contractions ending in "n't", then reverses the polarity of the next sentiment word at 75% strength. Intensifier detection recognizes amplifiers like "very" (1.5×), "extremely" (2.0×), and "really" (1.3×), scaling the next sentiment word's score accordingly.
The comparative score is the total score divided by the word count, normalizing for text length. This allows meaningful comparison between a 10-word tweet and a 500-word article. Sentences are individually scored and color-coded for visual scanning: green backgrounds indicate positive sentiment, red backgrounds indicate negative sentiment.
Accuracy and Limitations
Lexicon-based approaches achieve 70-80% accuracy on standard benchmarks like the Stanford Sentiment Treebank and IMDB review dataset. They perform best on explicitly emotional text (product reviews, opinion articles) and worst on subtle or contextual sentiment. Known limitations include: sarcasm ("what a great way to ruin my day"), domain-specific polarity ("sick" is negative medically but positive in slang), multi-word expressions where individual word scores don't capture phrase meaning, and non-English text. For language detection, see Language Detector.
Why Use This Tool
Instant Browser-Side Analysis
The entire AFINN lexicon is embedded in the page code (~15KB). Analysis runs in JavaScript with zero network calls, no API keys, no rate limits, and no usage quotas. Text never leaves your browser. Processing is instantaneous even for documents of several thousand words.
Common Use Cases
- Customer feedback triage: Quickly sort product reviews, support tickets, or survey responses by sentiment to prioritize negative feedback that needs immediate attention.
- Content tone checking: Verify that marketing copy, emails, or social media posts convey the intended emotional tone before publishing.
- Brand monitoring: Analyze batches of social media mentions or news articles to gauge overall public sentiment toward a brand, product, or campaign.
- Academic research: Use as a baseline sentiment measure for corpus analysis in linguistics, political science, or communication studies.
- Writing improvement: Identify unintentionally negative or positive language in professional documents where a neutral tone is desired.
Privacy
100% client-side processing. Your text is analyzed entirely in your browser using the embedded AFINN lexicon. No data is transmitted over the network. Related tools: Keyword Extractor, Readability Analyzer, Language Detector, and Word Counter.