\n Welcome to the Radical Text Analyser, mate!\n

Radical Text Analyser

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

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

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Analysing... Hang tight, mate!

\n $\"Under$ \n This site is always under construction!\n

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!

\n View Source\n

\n \n \n \n `);\n});\n\napp.post(\"/analyse\", async (c) => {\n const { text } = await c.req.json();\n\n if (text.length > 100000) {\n return c.json({ error: \"Text too long. Please limit to 100,000 characters.\" }, 400);\n }\n\n const words = text.trim().split(/\\s+/).length;\n const characters = text.length;\n\n // Calculate TF-IDF\n const tfidfScores = calculateTFIDF(text, 7);\n\n // Calculate word frequency\n const frequency = calculateWordFrequency(text, 30);\n\n // Generate fun fact\n const funFact = generateFunFact(text);\n\n // Perform sentiment analysis\n const sentimentAnalysis = analyseSentiment(text);\n\n // Generate summary\n const summary = generateSummary(text);\n\n return c.json({ words, characters, tfidf: tfidfScores, frequency, funFact, sentiment: sentimentAnalysis, summary });\n});\n\nexport default async function(request: Request): Promise {\n return app.fetch(request);\n}","deleted","label",[99],"permalink","https://www.val.town/x/mikehiggins/tfidf_analyser_90s_version/code/main.tsx","extension","actionData","errors"]