#demo #sentiment-analysis #internet #sentiment #slang

bin+lib vader_sentiment

Bindings for Rust from the original Python VaderSentiment analysis tool

2 releases

Uses old Rust 2015

0.1.1 Dec 1, 2021
0.1.0 Feb 7, 2021
Download history 149/week @ 2023-01-26 153/week @ 2023-02-02 126/week @ 2023-02-09 166/week @ 2023-02-16 324/week @ 2023-02-23 223/week @ 2023-03-02 218/week @ 2023-03-09 154/week @ 2023-03-16 228/week @ 2023-03-23 176/week @ 2023-03-30 148/week @ 2023-04-06 134/week @ 2023-04-13 93/week @ 2023-04-20 107/week @ 2023-04-27 198/week @ 2023-05-04 94/week @ 2023-05-11

519 downloads per month
Used in 2 crates

MIT license

522 lines


VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. It is fully open-sourced under the MIT License. This is a port of the original module, which was written in Python. If you'd like to make a contribution, please checkout the original author's work here.

Use Cases

* examples of typical use cases for sentiment analysis, including proper handling of sentences with:

	- typical negations (e.g., "not good")
	- use of contractions as negations (e.g., "wasn't very good")
	- conventional use of punctuation to signal increased sentiment intensity (e.g., "Good!!!")
	- conventional use of word-shape to signal emphasis (e.g., using ALL CAPS for words/phrases)
	- using degree modifiers to alter sentiment intensity (e.g., intensity boosters such as "very" and intensity dampeners such as "kind of")
	- understanding many sentiment-laden slang words (e.g., 'sux')
	- understanding many sentiment-laden slang words as modifiers such as 'uber' or 'friggin' or 'kinda'
	- understanding many sentiment-laden emoticons such as :) and :D
	- translating utf-8 encoded emojis such as 💘 and 💋 and 😁
	- understanding sentiment-laden initialisms and acronyms (for example: 'lol')

* more examples of tricky sentences that confuse other sentiment analysis tools
* example for how VADER can work in conjunction with NLTK to do sentiment analysis on longer texts...i.e., decomposing paragraphs, articles/reports/publications, or novels into sentence-level analyses
* examples of a concept for assessing the sentiment of images, video, or other tagged multimedia content
* if you have access to the Internet, the demo has an example of how VADER can work with analyzing sentiment of texts in other languages (non-English text sentences).



  extern crate vader_sentiment;

  fn main() {
      let analyzer = vader_sentiment::SentimentIntensityAnalyzer::new();
      println!("{:#?}", analyzer.polarity_scores("VADER is smart, handsome, and funny."));
      println!("{:#?}", analyzer.polarity_scores("VADER is VERY SMART, handsome, and FUNNY."));


    "compound": 0.8316320352807864,
    "pos": 0.7457627118644068,
    "neg": 0.0,
    "neu": 0.2542372881355932
    "compound": 0.9226571915792521,
    "pos": 0.7540988645515071,
    "neg": 0.0,
    "neu": 0.24590113544849293

Citation Information

If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Rust code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:

Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

For questions, please contact: C.J. Hutto Georgia Institute of Technology, Atlanta, GA 30032
cjhutto [at] gatech [dot] edu


You can run a full demo including cases with sarcasm, negation, idioms, and punctuation with this code.

extern crate vader_sentiment;

fn main() {


~41K SLoC