Effectively Check Website Content With The Best Cosine Similarity Checker

We'll use Cosine Similarity checker in JavaScript and HTML to create a text similarity tester in this tutorial.

What is the definition of cosine similarity?

The similarity of two vectors is measured using cosine similarity. It calculates the cosine of the angle formed by two vectors projected in three dimensions. This statistic may be used to evaluate how similar two texts are, regardless of their size.

Using the aforementioned method on the vector representation of each text's word count, the cosine similarity between two texts may be calculated. We can then compare the word counts of the two texts to see how similar they are.

Implementation

We'll write all of our functions in JavaScript, with some HTML and MaterializeCSS style thrown in for good measure.

The first step is to assign a frequency count to each word in a text or LDA. To do so, we simply divide the text into words, loop through each word, and count the number of times each word appears in the text.

This is exactly what the above function does, and it produces an object that has a mapping between a word and its frequency.

Next, we'll need to create a dictionary of all the terms that appear in each of the texts we're comparing. The word counts will then be represented as a vector using our dictionary. We create a function to take terms from our word-frequency mapping and add them to our dictionary to make the process of creating dictionaries easier.

Using our dictionary, we can now convert our word count map to a vector. Our vectors' dimensions will be determined by the amount of words in our dictionary. You can also opt for our other Keyword density check tools!

We can begin calculating their cosine similarity now that we have the code to convert text stings to vectors. As you may recall, cosine similarity is calculated by dividing the dot products of the two vectors by the product of their magnitudes. To compute the cosine similarity, we add three additional functions.

Now that we have everything we need, let's make our lives a little simpler by introducing a method that accepts two strings instead of vectors.

Let's start with a simple user interface. We'll use HTML and MaterializeCSS to style it.

The getSimilarityScore function is just used to round up the findings and convert them to percentages so that they are easier to interpret. When the Compare button is pressed, the last function is run, and it manipulates the DOM with jQuery to display the result.

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