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analyzeText runs a comprehensive analysis on a Persian string. The result is a deeply structured object: statistics, ratios, readability, language detection, sentiment, keywords, style, and editorial suggestions. Convenience helpers exist for one-shot lookups.
The main function is analyzeText, not textAnalyzer.

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Main function

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analyzeText(text, options?)

import { analyzeText } from "@persian-tools/persian-tools";

const a = analyzeText("این یک متن فارسی است.");
a.statistics.totalWords; // 5
a.statistics.totalCharacters; // 20
a.statistics.persianCharacters; // 15
a.language.primaryLanguage; // "persian"
a.language.confidence; // 95
a.language.isPurePersian; // true
a.readability.complexity; // "ساده"
a.readability.readingTime; // 1 (minutes)
a.readability.averageWordsPerSentence; // 5
The result is deeply nested — there is no flat { characters, words, lines } shape.

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Result shape

interface TextAnalysisResult {
  originalText: string;
  cleanedText: string;
  statistics: TextStatistics;       // counts: chars / words / sentences / paragraphs / numbers / ...
  ratios: TextRatios;               // persianRatio, arabicRatio, englishRatio, vowelRatio, ...
  readability: ReadabilityMetrics;  // complexity, readingTime, avg sentence length, ...
  language: LanguageDetection;      // primaryLanguage, confidence, isPurePersian
  sentiment: SentimentAnalysis;     // overall sentiment + indicator hits
  keywords: KeywordAnalysis;        // top keywords + frequency
  style: StyleAnalysis;             // formal / informal / technical
  suggestions: string[];
  quality: ...;
}
The individual interfaces are all exported.

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Convenience helpers

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getTextSummary(text)

import { getTextSummary } from "@persian-tools/persian-tools";

getTextSummary("سلام دنیا");
// "متن شامل 2 کلمه در 1 جمله است. زبان اصلی: فارسی (100% اطمینان). زمان مطالعه تقریبی: 1 دقیقه."

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getTextComplexity(text)

import { getTextComplexity } from "@persian-tools/persian-tools";

getTextComplexity("این جمله ساده است"); // "ساده" | "متوسط" | "پیچیده"

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getTextSentiment(text)

import { getTextSentiment } from "@persian-tools/persian-tools";

getTextSentiment("امروز روز خوبی بود"); // "مثبت" | "منفی" | "خنثی"

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getTextKeywords(text, limit?)

import { getTextKeywords } from "@persian-tools/persian-tools";

getTextKeywords(longArticle, 5); // string[]

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cleanText(text) & normalizeText(text)

import { cleanText, normalizeText } from "@persian-tools/persian-tools";

cleanText("سَلامٌ   123   دنیا"); // "سلام ۱۲۳ دنیا"
normalizeText(persianText); // internal match-key normalization
Use cleanText for display polish; normalizeText for equality comparisons across user variations.

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Performance

analyzeText is the heaviest utility in the library — it runs ~10 sub-analyses. Debounce for real-time per-keystroke usage. For batch jobs that need only one signal, prefer the dedicated helpers.

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Pitfalls

  • The function is analyzeText, not textAnalyzer.
  • Return shape is deeply nested. Access .statistics.totalWords, not .words.
  • isPurePersian: true requires no Arabic-specific letters and no other-language tokens; mixed posts return false.
  • Sentiment is rule-based. Acceptable for triage; do not present as fine-grained sentiment grading.

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Source

src/modules/textAnalyzer/index.ts, constants.ts · Tests: test/textAnalyzer.spec.ts