antoineaugusti/laravel-sentiment-analysis

A Laravel wrapper that performs sentiment analysis over an English sentence

Laravel Sentiment Analysis

, _(*1)

Introduction

A Laravel wrapper for phpInsight., _(*2)

Installation

PHP 7.2+, and Composer are required., _(*3)

To get the latest version of Laravel Sentiment Analysis, run the command composer require antoineaugusti/laravel-sentiment-analysis., _(*4)

You can register the SentimentAnalysis facade in the aliases key of your config/app.php file if you like., _(*5)

'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"., _(*6)

Looking for a Laravel 4 compatible version?

Checkout the 1.2 version, installable by requiring "antoineaugusti/laravel-sentiment-analysis": "1.2"., _(*7)

Usage

Sentences can be classified as negative, neutral or positive. The only supported language for the moment is English., _(*8)

Custom Dictionary

You can provide a custom dictionary by providing the path the folder when you create a new SentimentAnalysis object., _(*9)

$analysis = new SentimentAnalysis(storage_path('custom_dictionary/'));, _(*10)

Please look at the PHPInsight data files to see how you should name and structure your files., _(*11)

SentimentAnalysis::isNegative($sentence)

Returns a boolean telling if the given $sentence is classified as negative., _(*12)

SentimentAnalysis::isNeutral($sentence)

Returns a boolean telling if the given $sentence is classified as neutral., _(*13)

SentimentAnalysis::isPositive($sentence)

Returns a boolean telling if the given $sentence is classified as positive., _(*14)

SentimentAnalysis::decision($sentence)

Get the sentiment of a sentence. Will return negative, neutral or positive, _(*15)

SentimentAnalysis::score($sentence)

Get the confidence of a decision for a result. The closer to 1, the better!, _(*16)

SentimentAnalysis::scores($sentence)

Get the score value for each decision. Returns an array. The closer to 1, the better! Return example:, _(*17)

['negative' => 0.5, 'neutral' => 0.25, 'positive' => 0.25]