Word Mood Analyzer

What it actually does

Two lists of English words are written into the page's script. The script lowercases your text and, for each of the 29 listed words, asks a single question: does this sequence of letters appear anywhere in the text? Every list word that appears adds one to its side's total, no matter how often it appears. Then it compares the two totals.

That is the whole method. It is useful for one narrow job: spotting that a draft is carrying obviously loaded vocabulary, or showing a class how word choice reads on the page. It is not a measure of tone, intent or emotion, and it should not be described as one. The text stays in your browser; nothing is sent to a server.

The two word lists in full

These 29 entries are the entire vocabulary. Nothing else is recognised, and nothing is added or learned over time.

Two consequences follow from the list itself. The entry depress is a stem, so it also matches "depressed", "depressing" and "depression". And because positive and negative are themselves entries, a sentence that merely discusses tone — such as this one — can register a match on both sides.

The matching rule

1. The whole text is converted to lower case, so capitalisation is ignored.
2. Matching is on raw substrings, not whole words. There is no check for a space or punctuation at the edges, so a list word hidden inside a longer word still counts.
3. Each list word scores at most 1, however many times it occurs. "happy happy happy" gives a positive total of 1.
4. The highest possible totals are therefore 14 positive and 15 negative, whatever the length of the text.
5. More positive matches than negative gives "Positive"; more negative gives "Negative"; equal totals — including zero on both sides — gives "Neutral".

Worked examples

Each line below is the exact output of the function for that input.

The on-screen result also carries a face symbol and a colour — green, red or blue — after the word Positive, Negative or Neutral.

Where it goes wrong

• Negation. "not good", "never awful", "hardly amazing" and "no pain" all score as if the negating word were absent, so the result can be the exact opposite of the meaning.
• Sarcasm and understatement. "Fantastic, the server is down again" scores positive. Irony depends on context the script never sees.
• Words inside words. "painting" and "Spain" contain pain. "Badge" and "badminton" contain bad. "Crystal" contains cry. Each of those scores a match.
• Vocabulary outside the list. "Furious", "devastating", "appalling", "thrilled", "superb" and "grateful" are all invisible. A furious complaint using none of the 29 entries comes back Neutral.
• Non-English text. The lists are English, so Spanish, Hindi, Arabic or Chinese text normally returns 0 and 0 — unless an English letter sequence happens to appear inside a word.
• Who is being described. "I hate that she was treated badly" is about someone else's conduct, but it reads as negative wording from the writer.

Limits

There is no score, no percentage and no confidence figure — only two small integers and a comparison. The lists cannot be edited from the page, no weighting distinguishes "good" from "fantastic", and emoji, punctuation, intensifiers such as "very" and emphasis such as capital letters are all ignored. Sentence structure is ignored too: the text is treated as one undivided string, so the tool cannot say which sentence a match came from.

It also performs no spelling, grammar or readability check, and it does not rewrite your text or suggest alternatives.

Questions

Reviewed October 1, 2026. Calculation methods and corrections