Super simple sentiment analysis with AFINN.
npm i styled-preflight
yarn add styled-preflight
import { mindreader } from 'mindreader';
const sentiment = mindreader('good, bad');
console.log(sentiment);
/*
{
score: -3,
comparative: -1,
calculation: [{ ugly: -3 }, { bad: -3 }, { good: 3 }],
tokens: ['good', 'bad', 'ugly'],
words: ['ugly', 'bad', 'good],
positive: ['good']
negative: ['ugly', 'bad']
}
*/
mindreader
takes any string
input, for example:
import { mindreader } from 'mindreader';
const sentiment = mindreader(
'It was the best of times, it was the worst of times...'
);
...
The full output looks like this:
Name | Type | Description |
---|---|---|
score | number |
The overall sentiment score. Essentially just the sentiment ratings of each token, added up. |
comparative | number |
If you want to compare several strings, use this. It calculates the average score of all the tokens in your provided string. |
calculation | { [key: string]: number }[] |
How did we get here? This outputs the score which was assigned to each individual token in the string. If you want to do word-by-word sentiment analysis, passing in a big string to mindreader , and then reading from caluclation is the fastest way of doing it. |
tokens | string[] |
A list of the words in your string. |
words | string[] |
All the words which mindreader actually scanned. Some stopwords, like βtheβ, or rarer words will not be included here. |
positive | number |
A list of the positive marked words (those scored above 0 ) . |
negative | number |
A list of the positive marked words (those scored below 0 ) . |