https://github.com/syzer/sentiment-analyser

ML that can extract german and english sentiment
https://github.com/syzer/sentiment-analyser

english german nlp nlp-library node-js nodejs sentiment-analyser sentiment-analysis

Last synced: 14 days ago
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ML that can extract german and english sentiment

• Host: GitHub
• URL: https://github.com/syzer/sentiment-analyser
• Owner: syzer
• Created: 2015-12-02T18:11:07.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2021-01-21T20:10:21.000Z (almost 4 years ago)
• Last Synced: 2024-04-24T02:42:25.372Z (7 months ago)
• Topics: english, german, nlp, nlp-library, node-js, nodejs, sentiment-analyser, sentiment-analysis
• Language: JavaScript
• Homepage:
• Size: 101 KB
• Stars: 36
• Watchers: 3
• Forks: 12
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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# Wat

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Simple text sentiment analyser.

# Install

```sh

npm install --save ml-sentiment

```

### Usage

```js

const ml = require('ml-sentiment')();

ml.classify('Rainy day but still in a good mood');

//=> 2 ... (overall positive sentiment)

```

# How

Returns a positive number for positive sentiment association

and negative number for negative sentiment association.

#### Basics

```js

var longSentence = `Transform json to csv data. The difference to my other

module json2csv is json2csv-stream uses streams for transforming the incoming

data. The module is built with the new streaming API from Node.js v0.10.0 but

maintains backwards compatibility to earlier Node.js versions. Listen for

header and line events or pipe the data directly to a readable stream.`

const ml = require('ml-sentiment')();

ml.classify(longSentence);

//=> 0 ... (very boring encyclopedia like text)

ml.classify('Rainy day but still in a good mood');

//=> 2 ... (overall positive sentiment)

```

#### Negations

```js

const ml = require('ml-sentiment')();

ml.classify(`not awesome`);

//=> -3 (negative)

ml.classify(`awesome`);

//=> 3 (positive)

```

#### German

```js

const ml = require('ml-sentiment')({lang: 'de'});

ml.classify(`Es ist nicht so toll`);

//=> (negative)

```

# Credits

Original model and data:

Finn Årup Nielsen, "A new ANEW: Evaluation of a word list for

sentiment analysis in microblogs", http://arxiv.org/abs/1103.2903

For german model:

R. Remus, U. Quasthoff & G. Heyer: SentiWS - a Publicly Available German-language Resource for Sentiment Analysis.

In: Proceedings of the 7th International Language Ressources and Evaluation (LREC'10), 2010