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Dart Sentiment is a dart module that uses the AFINN-165 wordlist and Emoji Sentiment Ranking to perform sentiment analysis on arbitrary blocks of input text.

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Dart Sentiment

Flutter Community: dart_sentiment

pub package

AFINN-based sentiment analysis for dart

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. Dart Sentiment provides several things:

  • Provide Language support for English, Italian, French and German.
  • Provide support for various emojis.
  • Based on analysis of text, provide an integer value in the range -n to +n (see details below)

Installation

add following dependency to your pubspec.yaml

  
dependencies:    
   dart_sentiment: <latest-version>  
   

Example

 import 'package:dart_sentiment/dart_sentiment.dart';    
 void main() {
 
	 final sentiment = Sentiment();    
    
	 print(sentiment.analysis("The cake she made was terrible 😐"));    
    
	 print(sentiment.analysis("The cake she made was terrible 😐", emoji: true));    
    
	 print(sentiment.analysis("I love cats, but I am allergic to them.",));    
    
	 print(sentiment.analysis("J'adore les chats, mais j'y suis allergique.",    
	 languageCode: LanguageCode.french));    
    
	print(sentiment.analysis("Le gâteau qu'elle a fait était horrible 😐",    
	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,  
    tokens: [    
       "i",    
       "love",    
       "cats",    
       "but",    
       "i",    
       "am",    
       "allergic",    
       "to",    
       "them"    
    ],
    positive: [[love, 3]], 
    negative: [[allergic, 2]]
} 
  • Returned Objects
    • Score: Score calculated by adding the sentiment values of recognized words.
    • Comparative: Comparative score of the input string.
    • 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. 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

Contribute

If you have any suggestions, improvements or issues, feel free to contribute to this project. You
can either submit a new issue or propose a pull request. Direct your pull requests into the dev
branch.

License

Dart Sentiment is released under
the MIT License

Credit

Dart Sentiment inspired by the Javascript
package sentiment

About me

I am India based flutter developer

pub package twitter website

About

Dart Sentiment is a dart module that uses the AFINN-165 wordlist and Emoji Sentiment Ranking to perform sentiment analysis on arbitrary blocks of input text.

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