Content quality is one of the most heavily weighted ranking factors in Google's algorithm. But "quality" is not a subjective label — it can be measured. Search engines detect thin content (too few meaningful words), keyword stuffing (unnaturally high term frequency), semantic gaps (topics competitors cover that you ignore), and intent mismatch (content format doesn't match what searchers expect). Our tools make each of these measurable.

The foundation is accurate keyword frequency analysis. The Text Analyzer counts each word as its root lemma — so "buy", "buying", "bought" and "purchase" all merge into a single frequency count. This is how Google reads your content, not as isolated word forms. Without lemmatisation, keyword density figures are misleading: a page optimised for "running shoes" may under-report its coverage of "run" and "shoe" as separate signals.

Water content (stop-word ratio) measures the share of words that carry no semantic value: articles, prepositions, conjunctions, filler phrases like "it is worth noting that". The target range is 40–55%. Above 60%, your content is padded — it takes users longer to extract the information they came for, bounce rates rise, and Google interprets the page as lower quality than denser competitors.

Keyword nausea measures stuffing. Classic nausea is the square root of the most repeated word's count; academic nausea is the most frequent keyword as a share of meaningful words (the Advego formula). Both work on your text alone. To see which topics the top-ranking pages cover and yours does not, use Entity Coverage, which compares your page against the top results.

The Spell Checker adds a final layer of trust and quality. Spelling and grammar errors erode user confidence and increase bounce rate — both indirect ranking signals. More importantly, Google's Quality Rater Guidelines explicitly include written language quality in E-E-A-T evaluation. Pages with frequent errors are less likely to earn editorial backlinks and social shares, compounding the damage to organic performance.

Frequently Asked Questions

What is water content in text?

Water content (also called stop-word ratio) is the percentage of words in your text that carry no semantic meaning: pronouns, prepositions, conjunctions, modal verbs, and filler phrases. The healthy range is 40–55%. Below 40% and the text may feel robotic; above 60% it is padded and information-poor. High water content correlates with higher bounce rates and lower dwell time — both indirect quality signals for Google.

Does the Text Analyzer calculate TF-IDF?

No. TF-IDF compares how often a term appears in your document with a corpus of other pages, while the Text Analyzer works on a single text: lemma-level frequency, keyword density, water content and nausea. To compare your page with the top results for a query, use Entity Coverage.

What is keyword lemmatisation and why does it matter?

Lemmatisation reduces every word to its root dictionary form (lemma). "Runs", "running", "ran" all become "run". This is critical for accurate keyword density analysis because search engines evaluate semantic relevance at the lemma level, not the word-form level. Without lemmatisation, a page may appear to have 0.5% density for a keyword when the true lemma-level coverage is 2.1%.

What languages does the Text Analyzer support?

Russian (full morphological lemmatisation via pymorphy3), Ukrainian (pymorphy3 with Ukrainian dictionary), and English (Snowball stemmer with lemma lookup table). Language is detected automatically from the pasted text. The Spell Checker additionally supports all three languages via LanguageTool, with Yandex Speller as a secondary engine for Russian and Ukrainian to maximise error coverage.

How do I reduce water content without making text robotic?

Replace vague constructions with specific facts: instead of "it is important to note that our product provides significant value", write "the tool reduces analysis time by 80%". Eliminate passive voice where possible. Cut modal qualifiers ("may", "might", "could be considered") when the statement is factual. Replace generic nouns ("things", "aspects", "factors") with precise terms. Each of these changes increases information density without sacrificing readability.