The avatar for @nfigay
Figay
International Expert within the field of Enterprise Information System Interoperability, applied to Product Life Cycle Management, Model Based System Engineering and Virtual Manufacturing.
Public
Edited
May 1, 2024
Fork of Hello, NLP!
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words = toWords(nounsInfo)
.concat(toWords(verbsInfo))
.concat(toWords(adverbsInfo))
.concat(toWords(adjectivesInfo))
.sort((a,b) => b.freq - a.freq)
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doc = nlp(lyrics)
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normalizedDoc = nlp(lyrics).normalize({
whitespace: true, // remove hyphens, newlines, and force one space between words
punctuation: true, // remove commas, semicolons - but keep sentence-ending punctuation
case: true, // keep only first-word, and 'entity' titlecasing
numbers: true, // 'one' → '1'
plurals: true, // 'eyes' → 'eye'
verbs: true, // 'swtiched' → 'switch'
})
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sentences = doc.sentences().data()
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terms = doc.terms().data()
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ngrams = doc.ngrams().data()
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contractions = doc.contractions().data()
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tags = doc.out('tags')
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uniqueTags = getUniqueTags(tags)
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tagTree = createTagTree(uniqueTags)
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adjectives = doc.adjectives().data()
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adjectivesInfo = doc.adjectives().out('topk') // sort by frequency
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adjectiveList = toShortList(printList(adjectivesInfo))
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adverbs = doc.adverbs().data()
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adverbsInfo = doc.adverbs().out('topk')
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adverbList = toShortList(printList(adverbsInfo))
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nouns = doc.nouns().out('array')
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nounsInfo = normalizedDoc.nouns().out('topk') // sort by frequency
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nounList = toShortList(printList(nounsInfo))
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verbs = doc.verbs().out('array')
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verbsInfo = normalizedDoc.verbs().out('topk') // sort by frequency
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verbList = toShortList(printList(verbsInfo))
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nlp = require('compromise@11.14.3')
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import {printList, printHtml} from '@spencermountain/nlp-compromise'
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d3cloud = require('d3-cloud')
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import {createWordCloudSvg, downloadWordCloudSvg, toWords} from '@randomfractals/nlp-word-cloud'
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import {tagLegends, tagTypes, tagColors} from '@randomfractals/nlp-text-tags'
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import {createTagTreeSvg, downloadTagTreeSvg, getUniqueTags, createTagTree} from '@randomfractals/nlp-tag-tree'
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