antoineaugusti/laravel-sentiment-analysis
| Install | |
|---|---|
composer require antoineaugusti/laravel-sentiment-analysis |
|
| Latest Version: | v4.1 |
| PHP: | >=7.2 |
| License: | Apache-2.0 |
| Last Updated: | Feb 17, 2022 |
| Links: | GitHub · Packagist |
Laravel Sentiment Analysis
Introduction
A Laravel wrapper for phpInsight.
Installation
PHP 7.2+, and Composer are required.
To get the latest version of Laravel Sentiment Analysis, run the command composer require antoineaugusti/laravel-sentiment-analysis.
You can register the SentimentAnalysis facade in the aliases key of your config/app.php file if you like.
'SentimentAnalysis' => Antoineaugusti\LaravelSentimentAnalysis\SentimentAnalysis::class,
Looking for a Laravel 5 compatible version?
Checkout the 1.2 version, installable by requiring "antoineaugusti/laravel-sentiment-analysis": "2.2".
Looking for a Laravel 4 compatible version?
Checkout the 1.2 version, installable by requiring "antoineaugusti/laravel-sentiment-analysis": "1.2".
Usage
Sentences can be classified as negative, neutral or positive. The only supported language for the moment is English.
Custom Dictionary
You can provide a custom dictionary by providing the path the folder when you create a new SentimentAnalysis object.
$analysis = new SentimentAnalysis(storage_path('custom_dictionary/'));
Please look at the PHPInsight data files to see how you should name and structure your files.
SentimentAnalysis::isNegative($sentence)
Returns a boolean telling if the given $sentence is classified as negative.
SentimentAnalysis::isNeutral($sentence)
Returns a boolean telling if the given $sentence is classified as neutral.
SentimentAnalysis::isPositive($sentence)
Returns a boolean telling if the given $sentence is classified as positive.
SentimentAnalysis::decision($sentence)
Get the sentiment of a sentence. Will return negative, neutral or positive
SentimentAnalysis::score($sentence)
Get the confidence of a decision for a result. The closer to 1, the better!
SentimentAnalysis::scores($sentence)
Get the score value for each decision. Returns an array. The closer to 1, the better! Return example:
['negative' => 0.5, 'neutral' => 0.25, 'positive' => 0.25]