VaderSharp2 - The best sentiment analysis tool. In C#

"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."

Previously VADER was only available in python (https://github.com/cjhutto/vaderSentiment), and was then ported to C# in https://github.com/codingupastorm/vadersharp (this is a fork from this repos).

VaderSharp2 is an updated version of the original VaderSharp.

Citation Information (source)

If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python 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.

Changes since original port to C#

• Implement emojis
• Updated to the 3.3.2 Python version
• C# version now tracks Python versioning
• Targets netcoreapp3.1, netcoreapp2.1, net452, net46, net461, net462, net47, net5.0, net6.0
• In-depth testing

NOTE: There is a problem with how the _but_check function works in the python version - it uses sentiments.index(sentiment) on the double value... This results in unexpected results. This version has the correct behaviour.

Versioning

The VaderSharp2 follows the Python version, and the last number is the C# version, e.g. 3.3.2.1 is Python version 3.3.2 and C# version .1

Getting Started

To install VaderSharp, run the following command in the Package Manager Console:

Install-Package VaderSharp2

Usage

Import the package at the top of the page:

using VaderSharp2;

Then just initialize an instance of SentimentIntensityAnalyzer and call it's PolarityScores method:

var analyzer = new SentimentIntensityAnalyzer();

var results = analyzer.PolarityScores("Wow, this package is amazingly easy to use");

Console.WriteLine("Positive score: " + results.Positive);
Console.WriteLine("Negative score: " + results.Negative);
Console.WriteLine("Neutral score: " + results.Neutral);
Console.WriteLine("Compound score: " + results.Compound);

Code example

using VaderSharp2;

var sentences = new[]
{
    "VADER is smart, handsome, and funny.",  // positive sentence example
    "VADER is smart, handsome, and funny!",  // punctuation emphasis handled correctly (sentiment intensity adjusted)
    "VADER is very smart, handsome, and funny.", // booster words handled correctly (sentiment intensity adjusted)
    "VADER is VERY SMART, handsome, and FUNNY.",  //  emphasis for ALLCAPS handled
    "VADER is VERY SMART, handsome, and FUNNY!!!", // combination of signals - VADER appropriately adjusts intensity
    "VADER is VERY SMART, uber handsome, and FRIGGIN FUNNY!!!", //  booster words & punctuation make this close to ceiling for score
    "VADER is not smart, handsome, nor funny.",  // negation sentence example
    "The book was good.",  // positive sentence
    "At least it isn't a horrible book.",  // negated negative sentence with contraction
    "The book was only kind of good.", // qualified positive sentence is handled correctly (intensity adjusted)
    "The plot was good, but the characters are uncompelling and the dialog is not great.", // mixed negation sentence
    "Today SUX!",  // negative slang with capitalization emphasis
    "Today only kinda sux! But I'll get by, lol", // mixed sentiment example with slang and constrastive conjunction "but"
    "Make sure you :) or :D today!",  // emoticons handled
    "Catch utf-8 emoji such as such as 💘 and 💋 and 😁",  // emojis handled
    "Not bad at all"  // Capitalized negation
};

var analyzer = new SentimentIntensityAnalyzer();

foreach (var sentence in sentences)
{
    var vs = analyzer.PolarityScores(sentence);
    Console.WriteLine(Format(sentence, vs, 64));
}

static string Format(string sentence, SentimentAnalysisResults result, int dashesCount)
{
    string dashes = "";

    for (int i = 0; i < dashesCount - sentence.Length; i++)
    {
        dashes += "-";
    }

    return $"{sentence}{dashes} 'pos': {result.Positive}, 'compound': {result.Compound}, 'neu': {result.Neutral}, 'neg': {result.Negative}";
}

Output for the above example code

VADER is smart, handsome, and funny.---------------------------- 'pos': 0.746, 'compound': 0.8316, 'neu': 0.254, 'neg': 0
VADER is smart, handsome, and funny!---------------------------- 'pos': 0.752, 'compound': 0.8439, 'neu': 0.248, 'neg': 0
VADER is very smart, handsome, and funny.----------------------- 'pos': 0.701, 'compound': 0.8545, 'neu': 0.299, 'neg': 0
VADER is VERY SMART, handsome, and FUNNY.----------------------- 'pos': 0.754, 'compound': 0.9227, 'neu': 0.246, 'neg': 0
VADER is VERY SMART, handsome, and FUNNY!!!--------------------- 'pos': 0.767, 'compound': 0.9342, 'neu': 0.233, 'neg': 0
VADER is VERY SMART, uber handsome, and FRIGGIN FUNNY!!!-------- 'pos': 0.706, 'compound': 0.9469, 'neu': 0.294, 'neg': 0
VADER is not smart, handsome, nor funny.------------------------ 'pos': 0, 'compound': -0.7424, 'neu': 0.354, 'neg': 0.646
The book was good.---------------------------------------------- 'pos': 0.492, 'compound': 0.4404, 'neu': 0.508, 'neg': 0
At least it isn't a horrible book.------------------------------ 'pos': 0.322, 'compound': 0.431, 'neu': 0.678, 'neg': 0
The book was only kind of good.--------------------------------- 'pos': 0.303, 'compound': 0.3832, 'neu': 0.697, 'neg': 0
The plot was good, but the characters are uncompelling and the dialog is not great. 'pos': 0.094, 'compound': -0.7042, 'neu': 0.579, 'neg': 0.327
Today SUX!------------------------------------------------------ 'pos': 0, 'compound': -0.5461, 'neu': 0.221, 'neg': 0.779
Today only kinda sux! But I'll get by, lol---------------------- 'pos': 0.317, 'compound': 0.5249, 'neu': 0.556, 'neg': 0.127
Make sure you :) or :D today!----------------------------------- 'pos': 0.706, 'compound': 0.8633, 'neu': 0.294, 'neg': 0
Catch utf-8 emoji such as such as ?? and ?? and ??-------------- 'pos': 0.385, 'compound': 0.875, 'neu': 0.615, 'neg': 0
Not bad at all-------------------------------------------------- 'pos': 0.487, 'compound': 0.431, 'neu': 0.513, 'neg': 0

About the Scoring (source)

• The compound score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a 'normalized, weighted composite score' is accurate.

  It is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative.
  Typical threshold values (used in the literature cited on this page) are:

1. positive sentiment: compound score >= 0.05
2. neutral sentiment: (compound score > -0.05) and (compound score < 0.05)
3. negative sentiment: compound score <= -0.05

NOTE: The compound score is the one most commonly used for sentiment analysis by most researchers, including the authors.

• The pos, neu, and neg scores are ratios for proportions of text that fall in each category (so these should all add up to be 1... or close to it with float operation). These are the most useful metrics if you want to analyze the context & presentation of how sentiment is conveyed or embedded in rhetoric for a given sentence. For example, different writing styles may embed strongly positive or negative sentiment within varying proportions of neutral text -- i.e., some writing styles may reflect a penchant for strongly flavored rhetoric, whereas other styles may use a great deal of neutral text while still conveying a similar overall (compound) sentiment. As another example: researchers analyzing information presentation in journalistic or editorical news might desire to establish whether the proportions of text (associated with a topic or named entity, for example) are balanced with similar amounts of positively and negatively framed text versus being "biased" towards one polarity or the other for the topic/entity.

  • IMPORTANTLY: these proportions represent the "raw categorization" of each lexical item (e.g., words, emoticons/emojis, or initialisms) into positve, negative, or neutral classes; they do not account for the VADER rule-based enhancements such as word-order sensitivity for sentiment-laden multi-word phrases, degree modifiers, word-shape amplifiers, punctuation amplifiers, negation polarity switches, or contrastive conjunction sensitivity.