PHP Sentiment Analyzer is a lexicon and rule-based sentiment analysis tool that is used to understand sentiments in a sentence using VADER (Valence Aware Dictionary and sentiment Reasoner).
- Text
- Emoticon
- Emoji
- PHP 5.5 and above
Composer
Run the following to include this via Composer
composer require addedbytes/php-sentiment-analyzer
Use Sentiment\Analyzer;
$analyzer = new Analyzer();
$output_text = $analyzer->getSentiment("David is smart, handsome, and funny.");
$output_emoji = $analyzer->getSentiment("😁");
$output_text_with_emoji = $analyzer->getSentiment("Aproko doctor made me 🤣.");
print_r($output_text);
print_r($output_emoji);
print_r($output_text_with_emoji);David is smart, handsome, and funny. ---------------- ['neg'=> 0.0, 'neu'=> 0.337, 'pos'=> 0.663, 'compound'=> 0.7096]
😁 ------------------- ['neg' => 0, 'neu' => 0.5, 'pos' => 0.5, 'compound' => 0.4588]
Aproko doctor made me 🤣 ------------- ['neg' => 0, 'neu' => 0.714, 'pos' => 0.286, 'compound' => 0.4939]
You can now dynamically update the VADER (Valence) lexicon on the fly for words that are not in the dictionary. See the Example below:
Use Sentiment\Analyzer;
$sentiment = new Sentiment\Analyzer();
$strings = [
'Weather today is rubbish',
'This cake looks amazing',
'His skills are mediocre',
'He is very talented',
'She is seemingly very agressive',
'Marie was enthusiastic about the upcoming trip. Her brother was also passionate about her leaving - he would finally have the house for himself.',
'To be or not to be?',
];
//new words not in the dictionary
$newWords = [
'rubbish'=> '-1.5',
'mediocre' => '-1.0',
'agressive' => '-0.5'
];
//Dynamically update the dictionary with the new words
$sentiment->updateLexicon($newWords);
//Print results
foreach ($strings as $string) {
// calculations:
$scores = $sentiment->getSentiment($string);
// output:
echo "String: $string\n";
print_r(json_encode($scores));
echo "<br>";
}Weather today is rubbish ------------- {"neg":0.455,"neu":0.545,"pos":0,"compound":-0.3612}
This cake looks amazing ------------- {"neg":0,"neu":0.441,"pos":0.559,"compound":0.5859}
His skills are mediocre ------------- {"neg":0.4,"neu":0.6,"pos":0,"compound":-0.25}
He is very talented ------------- {"neg":0,"neu":0.457,"pos":0.543,"compound":0.552}
She is seemingly very agressive ------------- {"neg":0.338,"neu":0.662,"pos":0,"compound":-0.2598}
Marie was enthusiastic about the upcoming trip. Her brother was also passionate about her leaving - he would finally have the house for himself. ------------- {"neg":0,"neu":0.761,"pos":0.239,"compound":0.765}
String: To be or not to be? ------------- {"neg":0,"neu":1,"pos":0,"compound":0}
This package is licensed under the MIT license.
Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.