VADER-Sentiment-Analysis

NOTE: This fork of https://github.com/ckw017/vader-sentiment-rust. There have been no updates to the original codebase, and many dependencies are out of date. This renamed fork is primarily to make it compatible with the qsv CLI tool.

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).

Usage

Code

  use qsv_vader_sentiment_analysis;

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

Output

{
    "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

Demo

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

use qsv_vader_sentiment_analysis;

fn main() {
    qsv_vader_sentiment_analysis::demo::run_demo();
}