How to calculate the similarity of two articles using PHP

How to calculate the similarity of two articles using PHP？

Using the similar_text() function: This is a built-in function in PHP that calculates the similarity of two strings. It returns the number of matching characters in the two strings, or the similarity in percent if the third parameter is passed.

PHP

$similarity = similar_text($article1, $article2);

if ($similarity > 0.75) {
  echo "The two articles are very similar.";
} else {
  echo "The two articles are not very similar.";
}

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Using the levenshtein() function: This is another built-in function in PHP that calculates the Levenshtein distance between two strings. The Levenshtein distance is a measure of how similar two strings are, and it is calculated by counting the number of edits (insertions, deletions, or substitutions) required to transform one string into the other.

PHP

$levenshtein_distance = levenshtein($article1, $article2);

if ($levenshtein_distance < 10) {
  echo "The two articles are very similar.";
} else {
  echo "The two articles are not very similar.";
}

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Using a custom algorithm: You can also write your own custom algorithm to calculate the similarity of two articles. This could involve using a variety of factors, such as the number of common words, the number of common phrases, the order of the words, and the grammatical structure of the articles.

Once you have calculated the similarity of two articles, you can use it to do a variety of things, such as:

Detecting plagiarism: You can use the similarity of two articles to detect plagiarism. If the similarity is high, then it is likely that one of the articles is a plagiarized version of the other.
Recommending similar articles: You can use the similarity of two articles to recommend similar articles to users. For example, if a user reads an article about “how to cook a steak,” you could recommend other articles about “how to cook a chicken” or “how to cook a fish.”
Clustering articles: You can use the similarity of two articles to cluster articles together. This could be useful for organizing articles in a database or for creating a search engine.

I hope this helps!