There are different approaches to extract the most important keywords from a chunk of text, but one common method is to use natural language processing techniques such as tokenization, part-of-speech tagging, and frequency analysis. Here's an example of how you can use the Natural Language Toolkit (nltk) library in JavaScript to extract the most important keywords from a text:
import natural from 'natural';
import { removeStopwords, eng } from 'stopword';
const DEFAULT_COUNT = 5;
const DEFAULT_MIN_COUNT = 3;
const DEFAULT_MIN_LENGTH = 4;
const tokenizer = new natural.WordTokenizer();
const importantTags = new Set(['NN', 'NNS', 'NNP', 'NNPS', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']);
const language = 'EN';
const defaultCategory = 'N';
const defaultCategoryCapitalized = 'NNP';
const lexicon = new natural.Lexicon(language, defaultCategory, defaultCategoryCapitalized);
const ruleSet = new natural.RuleSet('EN');
const tagger = new natural.BrillPOSTagger(lexicon, ruleSet);
const toObjectList = (frequencyCounts) => {
return Object.keys(frequencyCounts).map((key) => {
return {
word: key,
count: frequencyCounts[key]
};
});
};
/**
* Analyze Text for the Top Keywords using NLTK/Natural.
* @param {*} param0
* @returns
*/
export const analyzeTopKeywords = ({
text,
count = DEFAULT_COUNT,
minCount = DEFAULT_MIN_COUNT,
minLength = DEFAULT_MIN_LENGTH
}) => {
const tokens = tokenizer.tokenize(text);
const filteredTokens = removeStopwords(tokens, eng);
const taggedWords = tagger.tag(filteredTokens).taggedWords;
const importantTokens = taggedWords.filter((token) => importantTags.has(token.tag)).map((token) => token.token);
// Count the frequency of each remaining word and sort them by frequency
const frequencyCounts = importantTokens.reduce((counts, token) => {
const key = token.toLowerCase();
counts[key] = (counts[key] || 0) + 1;
return counts;
}, {});
// Sort descending by count, return tokens that match
// a minimum occurrence count, and minimum word length.
const sortedKeywords = toObjectList(frequencyCounts)
.sort((a, b) => b.count - a.count)
.filter((item) => item.count >= minCount && item.word.length >= minLength);
const topKeywords = sortedKeywords.slice(0, count);
return topKeywords;
};
This code uses the natural library to tokenize the text into individual words, remove common stop words using the stopword library, perform part-of-speech tagging, filter out non-important parts of speech, count the frequency of each remaining word, and sort them by frequency. Finally, it outputs the top 5 most frequent keywords.
Note that this method is not perfect and may not always extract the most relevant keywords, but it provides a good starting point for keyword extraction. You may need to experiment with different techniques and adjust the parameters to get the best results for your specific use case.