Implementing Naive Bayes Classification algorithm into PHP to classify given text as ham or spam. To see complete project visit: https://github.com/varunon9/naive-bayes-classifier

Category.php

<?php

    class Category {
    	public static $HAM = 'ham';
    	public static $SPAM = 'spam';
    }

?>

db_connect.php

<?php
    /**
    * This file make connection to database using following parameters.
    */
    $servername = "localhost";
    $username = "root";
    $password = "password123";
    $dbname = "naiveBayes";

    // Create connection
    $conn = mysqli_connect($servername, $username, $password, $dbname);
    mysqli_set_charset($conn, "utf8");
    
    // Check connection
    if (mysqli_connect_errno()) {
        echo "Failed to connect to MySQL: " . mysqli_connect_error();
    }
?>

main.php

<?php

    /**
     * mysql> create database naiveBayes;
     * mysql> use naiveBayes;
     * mysql> create table trainingSet (S_NO integer primary key auto_increment, document text, category varchar(255));
     * mysql> create table wordFrequency (S_NO integer primary key auto_increment, word varchar(255), count integer, category varchar(255));
     */

    require_once('NaiveBayesClassifier.php');

    $classifier = new NaiveBayesClassifier();
    $spam = Category::$SPAM;
    $ham = Category::$HAM;

    $classifier -> train('Have a pleasurable stay! Get up to 30% off + Flat 20% Cashback on Oyo Room' . 
            ' bookings done via Paytm', $spam);
    $classifier -> train('Lets Talk Fashion! Get flat 40% Cashback on Backpacks, Watches, Perfumes,' .
            ' Sunglasses & more', $spam);

    $classifier -> train('Opportunity with Product firm for Fullstack | Backend | Frontend- Bangalore', $ham);
    $classifier -> train('Javascript Developer, Fullstack Developer in Bangalore- Urgent Requirement', $ham);

    $category = $classifier -> classify('Scan Paytm QR Code to Pay & Win 100% Cashback');
    echo $category;
    
    $category = $classifier -> classify('Re: Applying for Fullstack Developer');
    echo $category;

?>

NaiveBayesClassifier.php

<?php

    /**
     * @author Varun Kumar <varunon9@gmail.com>
     */
    
    require_once('Category.php');


    class NaiveBayesClassifier {

    	public function __construct() {
    	}

        /**
         * sentence is text(document) which will be classified as ham or spam
         * @return category- ham/spam
         */
    	public function classify($sentence) {

    		// extracting keywords from input text/sentence
    		$keywordsArray = $this -> tokenize($sentence);

    		// classifying the category
    		$category = $this -> decide($keywordsArray);

    		return $category;
    	}

    	/**
    	 * @sentence- text/document provided by user as training data
    	 * @category- category of sentence
    	 * this function will save sentence aka text/document in trainingSet table with given category
    	 * It will also update count of words (or insert new) in wordFrequency table
    	 */
    	public function train($sentence, $category) {
    		$spam = Category::$SPAM;
    		$ham = Category::$HAM;

    		if ($category == $spam || $category == $ham) {
            
	            //connecting to database
	    	    require 'db_connect.php';

	    	    // inserting sentence into trainingSet with given category
	    	    $sql = mysqli_query($conn, "INSERT into trainingSet (document, category) values('$sentence', '$category')");

	    	    // extracting keywords
	    	    $keywordsArray = $this -> tokenize($sentence);

	    	    // updating wordFrequency table
	    	    foreach ($keywordsArray as $word) {

	    	    	// if this word is already present with given category then update count else insert
	    	    	$sql = mysqli_query($conn, "SELECT count(*) as total FROM wordFrequency WHERE word = '$word' and category= '$category' ");
	    	    	$count = mysqli_fetch_assoc($sql);

	    	    	if ($count['total'] == 0) {
	    	    		$sql = mysqli_query($conn, "INSERT into wordFrequency (word, category, count) values('$word', '$category', 1)");
	    	    	} else {
	    	    		$sql = mysqli_query($conn, "UPDATE wordFrequency set count = count + 1 where word = '$word'");
	    	    	}
	    	    }

	    	    //closing connection
	    	    $conn -> close();

    		} else {
    			throw new Exception('Unknown category. Valid categories are: $ham, $spam');
    		}
    	}

    	/**
    	 * this function takes a paragraph of text as input and returns an array of keywords.
    	 */

    	private function tokenize($sentence) {
	        $stopWords = array('about','and','are','com','for','from','how',
	            'that','the','this', 'was','what','when','where','who','will','with','und','the','www');

	    	//removing all the characters which ar not letters, numbers or space
	    	$sentence = preg_replace("/[^a-zA-Z 0-9]+/", "", $sentence);

	    	//converting to lowercase
	    	$sentence = strtolower($sentence);

	        //an empty array
	    	$keywordsArray = array();

	    	//splitting text into array of keywords
	        //http://www.w3schools.com/php/func_string_strtok.asp
	    	$token =  strtok($sentence, " ");
	    	while ($token !== false) {

	    		//excluding elements of length less than 3
	    		if (!(strlen($token) <= 2)) {

	    			//excluding elements which are present in stopWords array
	                //http://www.w3schools.com/php/func_array_in_array.asp
	    			if (!(in_array($token, $stopWords))) {
	    				array_push($keywordsArray, $token);
	    			}
	    		}
		    	$token = strtok(" ");
	    	}
	    	return $keywordsArray;
    	}

    	/**
    	 * This function takes an array of words as input and return category (spam/ham) after
    	 * applying Naive Bayes Classifier
    	 *
    	 * Naive Bayes Classifier Algorithm -
    	 *
    	 *   p(spam/bodyText) = p(spam) * p(bodyText/spam) / p(bodyText);
    	 *   p(ham/bodyText) = p(ham) * p(bodyText/ham) / p(bodyText);
    	 *   p(bodyText) is constant so it can be ommitted
    	 *   p(spam) = no of documents (sentence) belonging to category spam / total no of documents (sentence)
    	 *   p(bodyText/spam) = p(word1/spam) * p(word2/spam) * .... p(wordn/spam)
    	 *   Laplace smoothing for such cases is usually given by (c+1)/(N+V), 
    	 *   where V is the vocabulary size (total no of different words)
    	 *   p(word/spam) = no of times word occur in spam / no of all words in spam
    	 *   Reference:
    	 *   http://stackoverflow.com/questions/9996327/using-a-naive-bayes-classifier-to-classify-tweets-some-problems
    	 *   https://github.com/ttezel/bayes/blob/master/lib/naive_bayes.js
    	*/
    	private function decide ($keywordsArray) {
    		$spam = Category::$SPAM;
    		$ham = Category::$HAM;

            // by default assuming category to be ham
    		$category = $ham;

    		// making connection to database
    	    require 'db_connect.php';

    		$sql = mysqli_query($conn, "SELECT count(*) as total FROM trainingSet WHERE  category = '$spam' ");
    		$spamCount = mysqli_fetch_assoc($sql);
    		$spamCount = $spamCount['total'];

    		$sql = mysqli_query($conn, "SELECT count(*) as total FROM trainingSet WHERE  category = '$ham' ");
    		$hamCount = mysqli_fetch_assoc($sql);
    		$hamCount = $hamCount['total'];

    		$sql = mysqli_query($conn, "SELECT count(*) as total FROM trainingSet ");
    		$totalCount = mysqli_fetch_assoc($sql);
    		$totalCount = $totalCount['total'];

    		//p(spam)
    		$pSpam = $spamCount / $totalCount; // (no of documents classified as spam / total no of documents)

    		//p(ham)
    		$pHam = $hamCount / $totalCount; // (no of documents classified as ham / total no of documents)
    		
    		//echo $pSpam." ".$pHam;
            
            // no of distinct words (used for laplace smoothing)
            $sql = mysqli_query($conn, "SELECT count(*) as total FROM wordFrequency ");
    		$distinctWords = mysqli_fetch_assoc($sql);
    		$distinctWords = $distinctWords['total'];

    		$bodyTextIsSpam = log($pSpam);
    		foreach ($keywordsArray as $word) {
    			$sql = mysqli_query($conn, "SELECT count as total FROM wordFrequency where word = '$word' and category = '$spam' ");
    			$wordCount = mysqli_fetch_assoc($sql);
    			$wordCount = $wordCount['total'];
    			$bodyTextIsSpam += log(($wordCount + 1) / ($spamCount + $distinctWords));
    		}

    		$bodyTextIsHam = log($pHam);
    		foreach ($keywordsArray as $word) {
    			$sql = mysqli_query($conn, "SELECT count as total FROM wordFrequency where word = '$word' and category = '$ham' ");
    			$wordCount = mysqli_fetch_assoc($sql);
    			$wordCount = $wordCount['total'];
    			$bodyTextIsHam += log(($wordCount + 1) / ($hamCount + $distinctWords));
    		}

    		if ($bodyTextIsHam >= $bodyTextIsSpam) {
    			$category = $ham;
    		} else {
    			$category = $spam;
    		}

    		$conn -> close();

    		return $category;
    	}
    }

?>