import { Hono } from "https://esm.sh/hono@3.10.2"; import { encode } from "https://esm.sh/js-base64@3.7.5"; const app = new Hono(); // Expanded stopwords list const stopwords = new Set([ "a", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any", "are", "as", "at", "be", "because", "been", "before", "being", "below", "between", "both", "but", "by", "could", "did", "do", "does", "doing", "down", "during", "each", "few", "for", "from", "further", "had", "has", "have", "having", "he", "her", "here", "hers", "herself", "him", "himself", "his", "how", "i", "if", "in", "into", "is", "it", "its", "itself", "just", "me", "more", "most", "my", "myself", "no", "nor", "not", "now", "of", "off", "on", "once", "only", "or", "other", "ought", "our", "ours", "ourselves", "out", "over", "own", "same", "she", "should", "so", "some", "such", "than", "that", "the", "their", "theirs", "them", "themselves", "then", "there", "these", "they", "this", "those", "through", "to", "too", "under", "until", "up", "very", "was", "we", "were", "what", "when", "where", "which", "while", "who", "whom", "why", "with", "would", "you", "your", "yours", "yourself", "yourselves" ]); // Function to calculate TF-IDF function calculateTFIDF(text, outputSize = 7) { const words = text.toLowerCase().match(/\b[a-z]{2,}\b/g) || []; const wordCount = words.length; const termFrequency = {}; const documentFrequency = {}; // Calculate term frequency and document frequency for (let i = 0; i < words.length; i++) { for (let j = 1; j <= 3 && i + j <= words.length; j++) { const phrase = words.slice(i, i + j).join(" "); if (!phrase.split(" ").every(word => stopwords.has(word))) { termFrequency[phrase] = (termFrequency[phrase] || 0) + 1; documentFrequency[phrase] = 1; } } } // Calculate TF-IDF const tfidf = {}; Object.keys(termFrequency).forEach(term => { const tf = termFrequency[term] / wordCount; const idf = Math.log(wordCount / (documentFrequency[term] || 1)) + 1; const termWordCount = term.split(" ").length; tfidf[term] = tf * idf * (termWordCount * 0.5) * (1 / Math.sqrt(termFrequency[term])) * (term.length / 10); }); // Sort by score (descending) and get top unique phrases const uniquePhrases = new Set(); return Object.entries(tfidf) .sort((a, b) => b[1] - a[1]) .filter(([phrase, _]) => { if (uniquePhrases.size >= outputSize) return false; const words = phrase.split(" "); if (words.length > 3) return false; if (words.some(word => word.length < 3)) return false; if (words.every(word => stopwords.has(word))) return false; if (Array.from(uniquePhrases).some(existingPhrase => existingPhrase.includes(phrase))) return false; uniquePhrases.add(phrase); return true; }) .slice(0, outputSize); } // Function to calculate word frequency function calculateWordFrequency(text, topN = 30) { const words = text.toLowerCase().match(/\b[a-z]{3,}\b/g) || []; const frequency = {}; words.forEach(word => { if (!stopwords.has(word)) { frequency[word] = (frequency[word] || 0) + 1; } }); return Object.entries(frequency) .sort((a, b) => b[1] - a[1]) .slice(0, topN); } // Function to generate a fun fact function generateFunFact(text) { const wordCount = text.trim().split(/\s+/).length; const charCount = text.length; const sentences = text.split(/[.!?]+/).length; const avgWordLength = wordCount > 0 ? charCount / wordCount : 0; const words = text.match(/\b\w+\b/g) || []; const longestWord = words.length > 0 ? words.reduce((a, b) => a.length > b.length ? a : b) : "N/A"; const facts = [ `Your text has an average word length of ${avgWordLength.toFixed(2)} characters. Shakespeare's average was about 3.99!`, `You've written ${sentences} sentence${sentences !== 1 ? "s" : ""}. A typical novel has about 50-60 sentences per page.`, `Your text is ${wordCount} words long. "War and Peace" by Leo Tolstoy is about 587,287 words!`, `Your longest word is "${longestWord}". The longest word in English is 189,819 letters long!`, `You've used ${charCount} characters. That's about ${(charCount / 280).toFixed(2)} tweets' worth of content!`, ]; return facts[Math.floor(Math.random() * facts.length)]; } // Function to perform simple sentiment analysis function analyseSentiment(text) { const positiveWords = new Set([ "good", "great", "excellent", "wonderful", "amazing", "fantastic", "awesome", "happy", "joy", "love", ]); const negativeWords = new Set([ "bad", "terrible", "awful", "horrible", "dreadful", "poor", "disappointing", "sad", "unhappy", "hate", ]); const words = text.toLowerCase().match(/\b\w+\b/g) || []; let score = 0; words.forEach(word => { if (positiveWords.has(word)) score++; if (negativeWords.has(word)) score--; }); let sentiment; if (score > 0) { sentiment = "positive"; } else if (score < 0) { sentiment = "negative"; } else { sentiment = "neutral"; } return { score, sentiment }; } // Function to generate a simple summary function generateSummary(text, sentenceCount = 3) { const sentences = text.match(/[^\.!\?]+[\.!\?]+/g) || []; return sentences.slice(0, sentenceCount).join(" "); } app.get("/", (c) => { return c.html(` Radical Text Analyser

Welcome to the Radical Text Analyser, mate!

Radical Text Analyser

by Mike Higgins - https://mike-higgins.glitch.me

Enter your text below to get a totally brilliant analysis, mate!

Analysing... Hang tight, mate!

This site is always under construction!

TF-IDF (Term Frequency-Inverse Document Frequency) is a brilliant way to figure out how important a word or phrase is in a document. Higher scores mean more important words or phrases!

We show the top 7 TF-IDF scores because it's a good balance between providing enough information and not overwhelming the user. It's also considered a "lucky" number in many cultures!

View Source

`); }); app.post("/analyse", async (c) => { const { text } = await c.req.json(); if (text.length > 100000) { return c.json({ error: "Text too long. Please limit to 100,000 characters." }, 400); } const words = text.trim().split(/\s+/).length; const characters = text.length; // Calculate TF-IDF const tfidfScores = calculateTFIDF(text, 7); // Calculate word frequency const frequency = calculateWordFrequency(text, 30); // Generate fun fact const funFact = generateFunFact(text); // Perform sentiment analysis const sentimentAnalysis = analyseSentiment(text); // Generate summary const summary = generateSummary(text); return c.json({ words, characters, tfidf: tfidfScores, frequency, funFact, sentiment: sentimentAnalysis, summary }); }); export default async function(request: Request): Promise { return app.fetch(request); }

tfidf_analyser_90s_version

Version 0