PHP code example of permafrost-dev / text-classifier

    
        
<?php
require_once('vendor/autoload.php');

/* Start to develop here. Best regards https://php-download.com/ */

    

permafrost-dev / text-classifier example snippets

use Permafrost\TextClassifier\TextClassifier;
use Permafrost\TextClassifier\Classifiers\NaiveBayes;
use Permafrost\TextClassifier\Pipelines\TextProcessingPipeline;
use Permafrost\TextClassifier\Tokenizers\EmailAddressTokenizer;
use Permafrost\TextClassifier\Processors\EmailAddressNormalizer;

$processors = new TextProcessingPipeline([
    new EmailAddressNormalizer(),
]);

$tc = new TextClassifier($processors, [new EmailAddressTokenizer()], new NaiveBayes());
$tc = $tc->trainFromFile(__DIR__ . '/email-train.txt');

$emails = [
    '[email protected]',
    '[email protected]',
];

foreach ($emails as $email) {
    echo "classification for '$email': " . $tc->classify($email) . PHP_EOL;
}

use Skyeng\Lemmatizer;
use Permafrost\TextClassifier\TextClassifier;
use Permafrost\TextClassifier\Classifiers\NaiveBayes;
use Permafrost\TextClassifier\Processors\TextLemmatizer;
use Permafrost\TextClassifier\Tokenizers\BasicTokenizer;
use Permafrost\TextClassifier\Tokenizers\NGramTokenizer;
use Permafrost\TextClassifier\Processors\StopwordRemover;
use Permafrost\TextClassifier\Processors\BasicTextNormalizer;
use Permafrost\TextClassifier\Pipelines\TextProcessingPipeline;

//Use different processors for training and classifying.  Since we're using keyword tokens,
//add all lemmas for each token during training to increase the size of the training data.
$trainingProcessors = [new TextLemmatizer(new Lemmatizer()), new BasicTextNormalizer()];

//When classifying, let's remove stopwords in addition to basic text normalization, because
//we'll be processing phrases.
$classifyProcessors = [new StopwordRemover(), new BasicTextNormalizer()];

//Let's use a basic tokenizer (word-based tokens), and an NGram tokenizer, which creates 
//trigrams (N=3). This should give us a good mix of keywords and partial keywords to look
//for when classifying text.
$tokenizers = [new BasicTokenizer(), new NGramTokenizer(3)];

$textClassifier = new TextClassifier(
    new TextProcessingPipeline($trainingProcessors, $classifyProcessors),
    $tokenizers,
    new NaiveBayes() //use Naive-Bayes as the classifier
);

$textClassifier->trainFromFile(__DIR__ . '/sentiment-train.txt');

$phrases = [
    'this is fantastic',
    'this is terrible',
];

foreach ($phrases as $phrase) {
    echo $phrase . ' - ' . $textClassifier->classify($phrase) . PHP_EOL;
}