# cjhutto/vadersentiment

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4,939 stars · 1,060 forks · Python · mit

## Links

- GitHub: https://github.com/cjhutto/vaderSentiment
- awesome-repositories: https://awesome-repositories.com/repository/cjhutto-vadersentiment.md

## Description

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a rule-based, lexicon-driven sentiment analyzer that assigns polarity scores to text by matching words against a curated sentiment dictionary and applying linguistic heuristics. It processes text at the sentence level, returning a compound score normalized between -1 (negative) and +1 (positive) along with separate positive, neutral, and negative intensity breakdowns.

What distinguishes VADER from simpler lexicon models is its built-in grammatical rule engine. It adjusts scores for negation (e.g., “not good” reduces positivity), contractions (e.g., “wasn’t” triggers inversion), degree modifiers (e.g., “very” intensifies), and capitalization (uppercase words receive an emotional emphasis boost). The lexicon extends beyond standard dictionary terms to include slang, emoticons (e.g., “:-)“), and acronyms (e.g., “LOL”) common in social media, making it particularly attuned to informal digital communication.

The tool is designed for short-form texts such as tweets, comments, or reviews, but can also aggregate per-sentence scores from longer documents to provide granular emotional tone analysis. It performs text polarity scoring as its primary capability, combining lexical lookup with rule-based adjustments to produce a single normalized compound score. The Python implementation is straightforward to install via standard package managers and can be used directly from the command line or imported as a library for programmatic sentiment analysis.

## Tags

### Artificial Intelligence & ML

- [Lexicon-Based Sentiment Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-analysis-tools/lexicon-based-sentiment-analyzers.md) — A rule-based engine that scores text polarity and intensity by matching words against a curated sentiment lexicon and applying linguistic rules.
- [Contextual Linguistic Sentiment Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/linguistic-pattern-analysis/contextual-linguistic-sentiment-analysis.md) — Accounts for negations, contractions, degree modifiers, and capitalization to adjust sentiment scores.
- [Rule-Enhanced Analyzers](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-analysis-tools/lexicon-based-sentiment-analyzers/rule-enhanced-analyzers.md) — Analyzes emotional tone by matching words to an emotion dictionary and applying grammar rules. ([source](https://github.com/cjhutto/vaderSentiment/search))
- [Social Media Sentiment Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-analysis-tools/social-media-sentiment-analysis.md) — Analyzes social media text to identify emotional polarity and strength using a word list and rules. ([source](https://github.com/cjhutto/vaderSentiment/blob/master/README.rst))
- [Sentiment Polarity Scoring](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-polarity-scoring.md) — Scores the emotional tone of short texts by returning a combined score and breakdown of positive, neutral, and negative content. ([source](https://github.com/cjhutto/vaderSentiment#readme))
- [Long-Form Sentiment Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-analysis-tools/long-form-sentiment-analysis.md) — Breaks long documents into sentence-level units for detailed emotional tone analysis.
- [Short-Form Scorers](https://awesome-repositories.com/f/artificial-intelligence-ml/sentiment-polarity-scoring/short-form-scorers.md) — Scores sentiment for brief digital messages such as tweets, comments, or reviews that include slang, emoticons, and acronyms.
- [Social Media Lexicons](https://awesome-repositories.com/f/artificial-intelligence-ml/social-media-lexicons.md) — Incorporates slang, emoticons, and acronyms common in digital communication into the sentiment vocabulary.

### Data & Databases

- [Sentiment Score](https://awesome-repositories.com/f/data-databases/metric-calculators/normalization/sentiment-score.md) — Combines positive, negative, and neutral ratios into a single compound score normalized between -1 and +1.

### Software Engineering & Architecture

- [Sentiment Rule Modifiers](https://awesome-repositories.com/f/software-engineering-architecture/access-rule-management/in-place-rule-modifiers/sentiment-rule-modifiers.md) — Adjusts sentiment scores using rules for negations, contractions, degree modifiers, and capitalization.

### Part of an Awesome List

- [Natural Language Processing](https://awesome-repositories.com/f/awesome-lists/ai/natural-language-processing.md) — Rule-based sentiment analysis tool for social media text.