Word Frequency Analyzer

Analyze and count word frequency in text.

Input

Output

Examples

Example 1

The quick brown fox jumps over the lazy dog. The dog was very lazy.

Understanding Word Frequency Analysis

Word frequency analysis counts how often each word appears in text, revealing patterns in language use. High-frequency words indicate central themes and topics, while rare words add variety and specificity. This statistical approach underpins SEO, content analysis, and natural language processing.

Frequency distribution follows Zipf's law: The most common word appears twice as often as the second, three times as the third, and so on. Function words like 'the', 'a', 'is' dominate, while content words (nouns, verbs, adjectives) reveal actual subject matter.

Stop word filtering improves analysis by removing common words that don't carry meaning. 'The', 'and', 'is' appear frequently but don't indicate topic. Filtering stop words highlights significant terms, making frequency analysis more useful for content insights.

Practical Applications of Frequency Analysis

SEO optimization uses word frequency to ensure target keywords appear sufficiently without over-optimization. A 2-3% keyword density balances relevance for search engines with readability for humans. Frequency analysis reveals if keywords are underused or spammed.

Content quality assessment identifies repetitive language. Writers unconsciously overuse favorite words or phrases. Frequency analysis surfaces these patterns, enabling more varied vocabulary and engaging content. Professional editors use this to refine style.

Academic plagiarism detection compares word frequency profiles between documents. Similar frequency distributions for uncommon words suggest copying. This statistical fingerprinting catches paraphrased plagiarism that exact-match tools miss.

Advanced Frequency Analysis Techniques

N-gram analysis extends beyond single words to phrases. Bigrams (two-word sequences) and trigrams capture common expressions like 'artificial intelligence' or 'climate change'. Phrase frequency reveals topics better than individual words alone.

TF-IDF (Term Frequency-Inverse Document Frequency) weights words by importance. Common words in a specific document but rare across all documents score high. This identifies distinctive vocabulary that differentiates one text from others.

Stemming and lemmatization group word variations: 'run', 'running', 'ran' all represent the same concept. Counting these together provides more accurate frequency for concepts rather than exact word forms, especially useful for analyzing different tenses and forms.

Weighted frequency considers context: Words in titles or headings carry more importance than body text. Search engines apply similar weighting. Frequency analyzers can assign different weights to measure actual impact beyond raw counts.

Best Practices for Word Frequency Analysis

Case normalization treats 'Apple' and 'apple' as the same word. Convert all text to lowercase before counting, unless case distinction matters (proper nouns vs common nouns). This prevents artificially inflated unique word counts.

Punctuation removal ensures 'word,' and 'word' count together. Strip punctuation marks before tokenizing to avoid treating the same word as different due to surrounding punctuation. Be cautious—contractions like "don't" need special handling.

Minimum frequency thresholds filter noise: Words appearing once or twice may be typos or insignificant. Set minimum occurrence counts (e.g., 3+) to focus on meaningful patterns. Balance between signal and completeness based on text length.

Visualize results with word clouds or frequency charts. Visual representations make patterns immediately obvious—dominant terms jump out, while long lists of numbers are hard to interpret. Tools like D3.js or Chart.js create effective frequency visualizations.

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