Free text analyzer for instant word count, character count, sentence analysis, and reading time. Perfect for writers, students, and content creators.
A text analyzer is a powerful yet simple tool designed to provide instant insights into any written content. Whether you're a writer, student, content creator, or editor, this text analyzer helps you understand key metrics about your text through comprehensive statistical analysis. By simply pasting or typing your text and clicking the "Analyze" button, you'll receive detailed information including word count, character count (both with and without spaces), sentence count, paragraph count, average words per sentence, the five most frequently used words, and an estimated reading time.
This tool requires no complex setup, external data sources, or technical knowledge—just straightforward text parsing and counting functions that deliver accurate results instantly. It's perfect for checking essay requirements, optimizing blog posts, analyzing document readability, or simply understanding the composition of any written content.
Enter Your Text: Locate the large text input area at the top of the application. You can either type your text directly or paste content from any source (documents, websites, emails, etc.).
Click Analyze: Once your text is entered, click the "Analyze" button below the input area.
Review Results: The analysis results will instantly appear below in an easy-to-read card layout, displaying all key statistics with clear labels and numbers.
Modify and Re-analyze: You can edit your text at any time and click "Analyze" again to see updated statistics.
Clear and Start Over: Simply delete the text or replace it with new content to analyze different text samples.
Note: The analyzer works with any language that uses standard word separators (spaces) and sentence punctuation, though reading time estimates are based on average English reading speeds.
The Text Analyzer handles various text formats and edge cases intelligently:
The tool is designed to be forgiving and handle real-world text input gracefully, providing meaningful results regardless of formatting inconsistencies.
The Text Analyzer provides the following comprehensive metrics:
Word Count: The total number of words in your text. Words are defined as sequences of characters separated by whitespace. Hyphenated words count as single words, as do contractions.
Character Count (With Spaces): The total number of characters including all letters, numbers, punctuation, and spaces. This gives you the absolute length of your text.
Character Count (Without Spaces): The total number of characters excluding all whitespace (spaces, tabs, line breaks). This metric is useful for platforms with character limits that don't count spaces.
Sentence Count: The number of sentences detected in your text. Sentences are identified by terminal punctuation marks (periods, exclamation points, question marks) followed by whitespace or end of text. The algorithm attempts to avoid false positives from abbreviations.
Paragraph Count: The number of paragraphs in your text. Paragraphs are separated by one or more line breaks. Even single-line text counts as one paragraph.
Average Words Per Sentence: Calculated by dividing the total word count by the sentence count. This metric helps assess readability—shorter averages typically indicate easier-to-read content.
Top 5 Most Frequent Words: A list of the five words that appear most often in your text, along with their occurrence counts. This helps identify key themes and potentially overused terms. Common articles and prepositions may dominate this list in natural text.
Reading Time Estimate: An approximation of how long it would take an average reader to read your text. This is calculated using a standard reading speed of approximately 200-250 words per minute for English text.
The Text Analyzer uses straightforward parsing and counting algorithms:
Word Counting:
Character Counting:
Sentence Detection:
Paragraph Counting:
Average Words Per Sentence:
Word Frequency Analysis:
Reading Time Calculation:
A text analyzer has versatile applications across many fields:
Academic Writing: Students can verify that essays and papers meet word count or character requirements, check sentence complexity, and ensure proper paragraph structure.
Content Creation & Blogging: Writers can optimize articles for SEO by targeting specific word counts, monitor readability through average sentence length, and identify overused keywords.
Social Media Management: Verify that posts meet platform-specific character limits (Twitter, LinkedIn, etc.) and optimize content length for engagement.
Professional Editing: Editors can quickly assess document statistics, identify verbose writing through high average words per sentence, and find repetitive word usage.
Email Composition: Ensure professional emails are concise by checking word counts and readability metrics before sending important communications.
Public Speaking: Calculate reading time to estimate speech duration when preparing presentations, lectures, or speeches from written scripts.
Translation & Localization: Compare source and translated text statistics to ensure consistency and completeness in translation projects.
Learning & Education: Language learners can analyze their writing to improve vocabulary diversity and sentence structure, while teachers can set specific metric targets for assignments.
While this Text Analyzer provides comprehensive basic metrics, other tools and approaches offer additional capabilities:
Readability Scores: Tools like Flesch-Kincaid, Gunning Fog Index, and SMOG provide numerical readability ratings based on syllable counts and sentence complexity.
Grammar Checkers: Applications like Grammarly and ProWritingAid offer advanced grammar checking, style suggestions, and writing enhancement features beyond basic statistics.
Keyword Density Analyzers: SEO-focused tools that calculate the percentage of specific keywords or phrases within content for search engine optimization.
Sentiment Analysis: Natural language processing tools that determine the emotional tone (positive, negative, neutral) of text content.
Plagiarism Checkers: Tools that compare text against databases to identify potential duplicate or copied content.
Text Summarizers: AI-powered tools that automatically generate condensed versions of longer texts while preserving key information.
Text analysis has evolved significantly alongside the development of computing technology. The earliest forms of text analysis were manual, with scholars and editors painstakingly counting words and characters by hand. The advent of mechanical typewriters in the late 19th century introduced character counters, providing the first automated text metrics.
The digital revolution of the 1960s and 1970s brought the first computerized text analysis tools. Early word processors like WordStar (1978) and WordPerfect (1979) included basic word counting features. As personal computers became widespread in the 1980s, text analysis capabilities became standard features in word processing software.
The 1990s saw the emergence of natural language processing (NLP) as a distinct field of computer science and artificial intelligence. Researchers developed algorithms for more sophisticated text analysis, including part-of-speech tagging, named entity recognition, and sentiment analysis. The internet boom created new demand for text analysis in search engines, content management systems, and web analytics.
In the 2000s, the rise of blogging and content marketing drove demand for SEO-focused text analysis tools. Word count and keyword density analyzers became essential tools for digital marketers and content creators. The proliferation of social media platforms in the late 2000s and 2010s introduced character-limited communication, making character counting an everyday concern for billions of users.
Today, text analysis encompasses a vast field ranging from simple counting metrics to advanced machine learning models that can understand context, emotion, and intent. Modern applications include automated content moderation, chatbots, voice assistants, and AI writing tools. Despite these advances, fundamental text statistics—word counts, character counts, and readability metrics—remain essential tools for writers, students, and professionals across all industries.
Here are implementation examples for text analysis functions in various programming languages:
1// JavaScript Text Analyzer Functions
2
3function analyzeText(text) {
4 if (!text || text.trim().length === 0) {
5 return {
6 wordCount: 0,
7 charCountWithSpaces: 0,
8 charCountWithoutSpaces: 0,
9 sentenceCount: 0,
10 paragraphCount: 0,
11 avgWordsPerSentence: 0,
12 topWords: [],
13 readingTime: '0 seconds'
14 };
15 }
16
17 const words = text.trim().split(/\s+/).filter(word => word.length > 0);
18 const wordCount = words.length;
19 const charCountWithSpaces = text.length;
20 const charCountWithoutSpaces = text.replace(/\s+/g, '').length;
21
22 // Count sentences (basic implementation)
23 const sentenceCount = Math.max(1, (text.match(/[.!?]+/g) || []).length);
24
25 // Count paragraphs
26 const paragraphs = text.split(/\n+/).filter(p => p.trim().length > 0);
27 const paragraphCount = Math.max(1, paragraphs.length);
28
29 // Calculate average words per sentence
30 const avgWordsPerSentence = (wordCount / sentenceCount).toFixed(1);
31
32 // Find top 5 frequent words
33 const wordFrequency = {};
34 words.forEach(word => {
35 const lowerWord = word.toLowerCase().replace(/[^a-z0-9]/g, '');
36 if (lowerWord) {
37 wordFrequency[lowerWord] = (wordFrequency[lowerWord] || 0) + 1;
38 }
39 });
40
41 const topWords = Object.entries(wordFrequency)
42 .sort((a, b) => b[1] - a[1])
43 .slice(0, 5)
44 .map(([word, count]) => ({ word, count }));
45
46 // Calculate reading time (225 words per minute)
47 const minutes = Math.floor(wordCount / 225);
48 const seconds = Math.round((wordCount % 225) / 225 * 60);
49 const readingTime = minutes > 0
50 ? `${minutes} min ${seconds} sec`
51 : `${seconds} seconds`;
52
53 return {
54 wordCount,
55 charCountWithSpaces,
56 charCountWithoutSpaces,
57 sentenceCount,
58 paragraphCount,
59 avgWordsPerSentence: parseFloat(avgWordsPerSentence),
60 topWords,
61 readingTime
62 };
63}
64
65// Example usage:
66const sampleText = "Hello world! This is a text analyzer. It counts words and more.";
67const results = analyzeText(sampleText);
68console.log(results);
691import re
2from collections import Counter
3
4def analyze_text(text):
5 if not text or not text.strip():
6 return {
7 'word_count': 0,
8 'char_count_with_spaces': 0,
9 'char_count_without_spaces': 0,
10 'sentence_count': 0,
11 'paragraph_count': 0,
12 'avg_words_per_sentence': 0,
13 'top_words': [],
14 'reading_time': '0 seconds'
15 }
16
17 # Word count
18 words = text.split()
19 word_count = len(words)
20
21 # Character counts
22 char_count_with_spaces = len(text)
23 char_count_without_spaces = len(re.sub(r'\s+', '', text))
24
25 # Sentence count
26 sentences = re.findall(r'[.!?]+', text)
27 sentence_count = max(1, len(sentences))
28
29 # Paragraph count
30 paragraphs = [p for p in text.split('\n') if p.strip()]
31 paragraph_count = max(1, len(paragraphs))
32
33 # Average words per sentence
34 avg_words_per_sentence = round(word_count / sentence_count, 1)
35
36 # Top 5 frequent words
37 clean_words = [re.sub(r'[^a-z0-9]', '', word.lower())
38 for word in words]
39 clean_words = [w for w in clean_words if w]
40 word_freq = Counter(clean_words)
41 top_words = [{'word': word, 'count': count}
42 for word, count in word_freq.most_common(5)]
43
44 # Reading time (225 words per minute)
45 minutes = word_count // 225
46 seconds = round((word_count % 225) / 225 * 60)
47 reading_time = f"{minutes} min {seconds} sec" if minutes > 0 else f"{seconds} seconds"
48
49 return {
50 'word_count': word_count,
51 'char_count_with_spaces': char_count_with_spaces,
52 'char_count_without_spaces': char_count_without_spaces,
53 'sentence_count': sentence_count,
54 'paragraph_count': paragraph_count,
55 'avg_words_per_sentence': avg_words_per_sentence,
56 'top_words': top_words,
57 'reading_time': reading_time
58 }
59
60# Example usage:
61sample_text = "Hello world! This is a text analyzer. It counts words and more."
62results = analyze_text(sample_text)
63print(results)
641import java.util.*;
2import java.util.regex.*;
3import java.util.stream.*;
4
5public class TextAnalyzer {
6
7 public static class AnalysisResult {
8 public int wordCount;
9 public int charCountWithSpaces;
10 public int charCountWithoutSpaces;
11 public int sentenceCount;
12 public int paragraphCount;
13 public double avgWordsPerSentence;
14 public List<WordFrequency> topWords;
15 public String readingTime;
16
17 public static class WordFrequency {
18 public String word;
19 public int count;
20
21 public WordFrequency(String word, int count) {
22 this.word = word;
23 this.count = count;
24 }
25 }
26 }
27
28 public static AnalysisResult analyzeText(String text) {
29 AnalysisResult result = new AnalysisResult();
30
31 if (text == null || text.trim().isEmpty()) {
32 result.wordCount = 0;
33 result.charCountWithSpaces = 0;
34 result.charCountWithoutSpaces = 0;
35 result.sentenceCount = 0;
36 result.paragraphCount = 0;
37 result.avgWordsPerSentence = 0;
38 result.topWords = new ArrayList<>();
39 result.readingTime = "0 seconds";
40 return result;
41 }
42
43 // Word count
44 String[] words = text.trim().split("\\s+");
45 result.wordCount = words.length;
46
47 // Character counts
48 result.charCountWithSpaces = text.length();
49 result.charCountWithoutSpaces = text.replaceAll("\\s+", "").length();
50
51 // Sentence count
52 Pattern sentencePattern = Pattern.compile("[.!?]+");
53 Matcher sentenceMatcher = sentencePattern.matcher(text);
54 result.sentenceCount = Math.max(1, (int) sentenceMatcher.results().count());
55
56 // Paragraph count
57 String[] paragraphs = text.split("\n+");
58 result.paragraphCount = Math.max(1,
59 (int) Arrays.stream(paragraphs).filter(p -> !p.trim().isEmpty()).count());
60
61 // Average words per sentence
62 result.avgWordsPerSentence =
63 Math.round((double) result.wordCount / result.sentenceCount * 10.0) / 10.0;
64
65 // Top 5 frequent words
66 Map<String, Integer> wordFreq = new HashMap<>();
67 for (String word : words) {
68 String cleanWord = word.toLowerCase().replaceAll("[^a-z0-9]", "");
69 if (!cleanWord.isEmpty()) {
70 wordFreq.put(cleanWord, wordFreq.getOrDefault(cleanWord, 0) + 1);
71 }
72 }
73
74 result.topWords = wordFreq.entrySet().stream()
75 .sorted(Map.Entry.<String, Integer>comparingByValue().reversed())
76 .limit(5)
77 .map(e -> new AnalysisResult.WordFrequency(e.getKey(), e.getValue()))
78 .collect(Collectors.toList());
79
80 // Reading time (225 words per minute)
81 int minutes = result.wordCount / 225;
82 int seconds = Math.round((result.wordCount % 225) / 225.0f * 60);
83 result.readingTime = minutes > 0
84 ? minutes + " min " + seconds + " sec"
85 : seconds + " seconds";
86
87 return result;
88 }
89
90 public static void main(String[] args) {
91 String sampleText = "Hello world! This is a text analyzer. It counts words and more.";
92 AnalysisResult results = analyzeText(sampleText);
93 System.out.println("Word Count: " + results.wordCount);
94 System.out.println("Reading Time: " + results.readingTime);
95 }
96}
971' Excel VBA Function for Text Analysis
2Function WordCount(text As String) As Long
3 Dim words() As String
4 If Len(Trim(text)) = 0 Then
5 WordCount = 0
6 Else
7 words = Split(Trim(text), " ")
8 WordCount = UBound(words) + 1
9 End If
10End Function
11
12Function CharCountWithSpaces(text As String) As Long
13 CharCountWithSpaces = Len(text)
14End Function
15
16Function CharCountWithoutSpaces(text As String) As Long
17 Dim textNoSpaces As String
18 textNoSpaces = Replace(text, " ", "")
19 textNoSpaces = Replace(textNoSpaces, vbTab, "")
20 textNoSpaces = Replace(textNoSpaces, vbCrLf, "")
21 textNoSpaces = Replace(textNoSpaces, vbCr, "")
22 textNoSpaces = Replace(textNoSpaces, vbLf, "")
23 CharCountWithoutSpaces = Len(textNoSpaces)
24End Function
25
26Function SentenceCount(text As String) As Long
27 Dim count As Long
28 Dim i As Long
29 count = 0
30
31 For i = 1 To Len(text)
32 If Mid(text, i, 1) = "." Or Mid(text, i, 1) = "!" Or Mid(text, i, 1) = "?" Then
33 count = count + 1
34 End If
35 Next i
36
37 If count = 0 And Len(Trim(text)) > 0 Then
38 count = 1
39 End If
40
41 SentenceCount = count
42End Function
43
44' Usage in Excel:
45' =WordCount(A1)
46' =CharCountWithSpaces(A1)
47' =CharCountWithoutSpaces(A1)
48' =SentenceCount(A1)
49These examples demonstrate how to implement the core text analysis functions in different programming languages. Each implementation can be adapted and extended based on specific requirements.
Here are several example text inputs and their corresponding analysis results:
Example 1: Short Paragraph
Input Text: "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet."
Analysis Results:
Example 2: Multi-paragraph Text
Input Text: "Hello world! This is the first paragraph.
This is the second paragraph with more content. It has multiple sentences to demonstrate the analyzer."
Analysis Results:
A text analyzer counts and analyzes various metrics in your text, including word count, character count (with and without spaces), sentence count, paragraph count, reading time, and word frequency. It provides instant statistical insights to help improve your writing and meet specific content requirements.
The text analyzer word count is highly accurate, using the same algorithms as major word processors. Words are counted by splitting text at whitespace boundaries, matching industry-standard counting methods used in Microsoft Word, Google Docs, and other professional tools.
Yes, the text analyzer is excellent for SEO optimization. You can check if your content meets target word counts, monitor readability through average sentence length, identify keyword frequency with the top words feature, and calculate reading time to optimize user engagement.
The text analyzer works best with languages that use spaces to separate words (English, Spanish, French, etc.). Character counting works for all languages, but word counting may be less accurate for languages without clear word boundaries (such as Chinese or Japanese).
While the text analyzer can process texts of various lengths from single words to multi-page documents, extremely long texts (over 100,000 characters) may experience slight processing delays. For typical use cases, the tool provides instant real-time results.
Reading time is calculated based on an average reading speed of 225 words per minute, which is standard for adult English readers. The estimate provides a close approximation for blog posts, articles, and general content, though actual reading speed varies by individual and content complexity.
The text analyzer identifies sentences by detecting terminal punctuation marks (periods, exclamation points, question marks) followed by whitespace or end of text. It includes basic logic to avoid counting abbreviations as sentence breaks, ensuring accurate sentence counts.
Absolutely. Students commonly use text analyzers to verify essays and papers meet word count or character requirements, check sentence complexity through average words per sentence, ensure proper paragraph structure, and confirm readability for academic standards.
Ready to optimize your writing? Use this free text analyzer tool to instantly get word counts, character counts, readability metrics, and more. Perfect for content creators, students, writers, and professionals who need accurate text statistics without complex software or signup requirements.
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