dart_sentiment 0.0.2

A new Flutter package project.

dart_sentiment

AFINN-based sentiment analysis for dart

Sentiment is a dart package that uses the AFINN-165 wordlist and Emoji Sentiment Ranking to perform sentiment analysis on arbitrary blocks of input text. Sentiment provides several things:

• Performance (see benchmarks below)
• The ability to append and overwrite word / value pairs from the AFINN wordlist
• The ability to easily add support for new languages
• The ability to easily define custom strategies for negation, emphasis, etc. on a per-language basis Some inline code.

Installation

Add this line to your pubspec.yaml and run flutter pub get command to activate the package

dependencies:
	dart_sentiment: ^0.0.1

Example

import 'package:dart_sentiment/dart_sentiment.dart';

void main() {

final sentiment = Sentiment();

print(sentiment.analysis(

'i love dart',

));

print(sentiment.analysis('i hate you piece of shit 💩'));

print(sentiment.analysis('i hate you piece of shit 💩', emoji: true));

print(sentiment.analysis('ti odio un pezzo di merda 💩',

languageCode: LanguageCode.italian));

print(sentiment.analysis('ti odio un pezzo di merda 💩',

emoji: true, languageCode: LanguageCode.italian));

print(sentiment.analysis('je te déteste merde 💩',

languageCode: LanguageCode.french));

print(sentiment.analysis('je te déteste merde 💩',

emoji: true, languageCode: LanguageCode.french));

}

Function defination

Param	Description
String text	Input phrase to analyze
bool emoji = false	Input emoji is present in the phrase to analyze
LanguageCode languageCode = LanguageCode.english	Language to use for sentiment analysis. LanguageCode { english, italian, french, german }

How it works

AFINN

AFINN is a list of words rated for valence with an integer between minus five (negative) and plus five (positive). Sentiment analysis is performed by cross-checking the string tokens (words, emojis) with the AFINN list and getting their respective scores. The comparative score is simply: sum of each token / number of tokens. So for example let's take the following:

I love cats, but I am allergic to them.

That string results in the following:

{
    score: 1,
    comparative: 0.1111111111111111,
    calculation: [ { allergic: -2 }, { love: 3 } ],
    tokens: [
        'i',
        'love',
        'cats',
        'but',
        'i',
        'am',
        'allergic',
        'to',
        'them'
    ],
    words: [
        'allergic',
        'love'
    ],
    positive: [
        'love'
    ],
    negative: [
        'allergic'
    ]
}

• Returned Objects
  • Score: Score calculated by adding the sentiment values of recognized words.
  • Comparative: Comparative score of the input string.
  • Calculation: An array of words that have a negative or positive valence with their respective AFINN score.
  • Token: All the tokens like words or emojis found in the input string.
  • Words: List of words from input string that were found in AFINN list.
  • Positive: List of positive words in input string that were found in AFINN list.
  • Negative: List of negative words in input string that were found in AFINN list.

In this case, love has a value of 3, allergic has a value of -2, and the remaining tokens are neutral with a value of 0. Because the string has 9 tokens the resulting comparative score looks like: (3 + -2) / 9 = 0.111111111

This approach leaves you with a mid-point of 0 and the upper and lower bounds are constrained to positive and negative 5 respectively (the same as each token! 😸). For example, let's imagine an incredibly "positive" string with 200 tokens and where each token has an AFINN score of 5. Our resulting comparative score would look like this:

(max positive score * number of tokens) / number of tokens
(5 * 200) / 200 = 5