EXPLORING TEXT SIMILARITY IN JAVASCRIPT

Dave Teu
Mastering Javascript
3 min readSep 8, 2023

Introduction

Measuring text similarity is a valuable task in a variety of applications, from plagiarism detection to content recommendation systems. In JavaScript, there are several techniques that can be used to determine the similarity between two texts. In this blog post, we will discuss three common approaches — Levenshtein Distance, Cosine Similarity, and Jaccard Similarity — and provide code examples for each.

Levenshtein Distance

The Levenshtein distance calculates the minimum number of single-character edits required to transform one text into another. It provides a basic measure of text similarity based on string differences.

We can implement the Levenshtein Distance algorithm in JavaScript using dynamic programming. The code snippet below demonstrates this approach:

function levenshteinDistance(a, b) {
const matrix = [];
for (let i = 0; i <= b.length; i++) {
matrix[i] = [i];
}
for (let j = 1; j <= a.length; j++) {
matrix[0][j] = j;
}
for (let i = 1; i <= b.length; i++) {
for (let j = 1; j <= a.length; j++) {
const indicator = a[j - 1] === b[i - 1] ? 0 : 1;
matrix[i][j] = Math.min(
matrix[i - 1][j] + 1, // deletion
matrix[i][j - 1] + 1, // insertion
matrix[i - 1][j - 1] + indicator // substitution
);
}
}
return matrix[b.length][a.length];
}
const text1 = 'Hello';
const text2 = 'Hallo';
const similarity = 1 - (levenshteinDistance(text1, text2) / Math.max(text1.length, text2.length));
console.log(similarity); // 0.8 (80% similarity)

As you can see, the Levenshtein Distance algorithm is relatively straightforward to implement in JavaScript. This makes it a good choice for applications where performance is not critical.

Cosine Similarity

Cosine Similarity measures the cosine of the angle between two text vectors, considering each text as a vector in a high-dimensional space. It evaluates the term frequency of words to determine the similarity.

To calculate Cosine Similarity, we can utilize existing libraries such as string-similarity. Here’s an example of calculating the Cosine Similarity using string-similarity:

const stringSimilarity = require('string-similarity');

const text1 = 'Hello world';
const text2 = 'Hello there';
const similarity = stringSimilarity.compareTwoStrings(text1, text2);
console.log(similarity); // 0.65 (65% similarity)

Cosine Similarity is a more sophisticated measure of text similarity than Levenshtein Distance. However, it can be more computationally expensive to calculate.

Jaccard Similarity

Jaccard Similarity quantifies text similarity based on the overlap of word sets between two texts. It computes the ratio of the intersection to the union of the sets.

We can implement Jaccard Similarity in JavaScript by utilizing libraries like natural or creating a manual implementation. Let’s see an example using the natural library:

const natural = require('natural');

const tokenizer = new natural.WordTokenizer();
const text1 = 'Hello world';
const text2 = 'Hello there';
const set1 = new Set(tokenizer.tokenize(text1));
const set2 = new Set(tokenizer.tokenize(text2));
const intersection = new Set([...set1].filter(x => set2.has(x)));
const union = new Set([...set1, ...set2]);
const similarity = intersection.size / union.size;
console.log(similarity); // 0.4 (40% similarity)

Jaccard Similarity is a simple and efficient measure of text similarity. However, it can be less accurate than Cosine Similarity in some cases.

Sure, here is the rest of the blog article:

Which Technique Should You Use?

The best technique for measuring text similarity depends on the specific requirements of your application. If performance is not critical, the Levenshtein Distance algorithm is a good choice because it is relatively straightforward to implement. If performance is critical, Cosine Similarity is a good choice because it is a more sophisticated measure of text similarity. Jaccard Similarity is a simple and efficient measure of text similarity, but it can be less accurate than Cosine Similarity in some cases.

Here is a table that summarizes the different text similarity techniques and their strengths and weaknesses:

TechniqueStrengthsWeaknessesLevenshtein DistanceStraightforward to implementNot as accurate as Cosine SimilarityCosine SimilarityMore accurate than Levenshtein DistanceMore computationally expensive to calculateJaccard SimilaritySimple and efficientNot as accurate as Cosine Similarity in some cases

Conclusion

Measuring text similarity is a valuable task in a variety of applications. In JavaScript, there are several techniques that can be used to determine the similarity between two texts. The best technique for your application depends on the specific requirements.

I hope this blog post has been helpful and informative. If you have any questions, please feel free to leave a comment below.

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