Text Sentiment Polarity Checker
Scores text with a small built-in positive/negative English word list and a short preceding-negation window. It does not use the AFINN-165 lexicon.
About this text sentiment polarity checker
Scores text with a small built-in positive/negative English word list and a short preceding-negation window. It does not use the AFINN-165 lexicon Applies Unicode-compliant text transformations and parsing directly in the browser tab without data leakage.
Text processing & transformation mechanics
Uses a small built-in English positive/negative word list. A matched word is treated as negated only when a supported negator appears among the preceding three tokens; it is not AFINN-165 or a general language model.
Worked text manipulation example
Scenario: Apply the small built-in English word lists to a short review.
Sample input:
Processing: Matches only its built-in English positive/negative words and treats a matching word as negated when a negator occurs among its preceding three tokens; it is not AFINN-165.
Illustrative output:
Character encoding & string length boundaries
This is a small built-in English word list, not AFINN-165 or a language model. Only ASCII letter/apostrophe tokens can match it; a negator among the prior three tokens changes the simple score. It cannot understand sarcasm, domain context, or sentiment in other languages; negative words under negation add only 0.5 positive points.
Text formatting and transformations in Text Sentiment Polarity Checker preserve character integrity. Check edge cases such as multi-line inputs and special characters before downstream use.
In-memory text processing boundary
String operations and Unicode manipulations in Text Sentiment Polarity Checker execute entirely in local browser memory without transmitting input text.
Unicode Consortium & text formatting standards
Unicode Consortium standard annexes (UAX) and character encoding specifications:
- ECMAScript ASCII letter-and-apostrophe token matching.
- CZOA-specific small English lexicon and three-prior-token negation rule; not AFINN-165 or a standardized sentiment classifier.
Text Sentiment Polarity Checker verification · Audit standards
How to use it
- Enter English text in the input box.
- Run locally to match a small built-in positive/negative word list and look for negators in the previous three tokens.
- Read or copy JSON counts, score, polarity, and per-word matches.
Frequently asked questions
Which words affect Sentiment Analyzer?+
It uses fixed, short English positive and negative word lists plus negators. In `not good but excellent`, both `good` and `excellent` fall within the three-token window after `not`, so each becomes one negative point. The result is `score: -2` and `polarity: "negative"`; `but` does not reset that window.
What local example can I run?+
Enter `not good but excellent`. The result is `positive: 0`, `negative: 2`, `score: -2`, and `polarity: "negative"`.
How are negated negative words scored?+
A negative word with a negator in the preceding three tokens adds positive `0.5` and is reported as `inverted-neutral`. A positive word in that window adds one negative point instead.
Is this a general-purpose sentiment model?+
No. It is a small deterministic English lexicon counter, not AFINN-165 or a machine-learning classifier. It is not reliable for nuance, sarcasm, multilingual text, or domain-specific language.