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cjhutto avatar

cjhutto/vaderSentiment

0
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4,939 stars·1,060 forks·Python·mit·10 views

VaderSentiment

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.

Features

  • Lexicon-Based Sentiment Analyzers - 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 - Accounts for negations, contractions, degree modifiers, and capitalization to adjust sentiment scores.
  • Rule-Enhanced Analyzers - Analyzes emotional tone by matching words to an emotion dictionary and applying grammar rules.
  • Social Media Sentiment Analysis - Analyzes social media text to identify emotional polarity and strength using a word list and rules.
  • Sentiment Polarity Scoring - Scores the emotional tone of short texts by returning a combined score and breakdown of positive, neutral, and negative content.
  • Sentiment Score - Combines positive, negative, and neutral ratios into a single compound score normalized between -1 and +1.
  • Sentiment Rule Modifiers - Adjusts sentiment scores using rules for negations, contractions, degree modifiers, and capitalization.
  • Long-Form Sentiment Analysis - Breaks long documents into sentence-level units for detailed emotional tone analysis.
  • Short-Form Scorers - Scores sentiment for brief digital messages such as tweets, comments, or reviews that include slang, emoticons, and acronyms.
  • Social Media Lexicons - Incorporates slang, emoticons, and acronyms common in digital communication into the sentiment vocabulary.
  • Natural Language Processing - Rule-based sentiment analysis tool for social media text.

Star history

Star history chart for cjhutto/vadersentimentStar history chart for cjhutto/vadersentiment

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does cjhutto/vadersentiment do?

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 are the main features of cjhutto/vadersentiment?

The main features of cjhutto/vadersentiment are: Lexicon-Based Sentiment Analyzers, Contextual Linguistic Sentiment Analysis, Rule-Enhanced Analyzers, Social Media Sentiment Analysis, Sentiment Polarity Scoring, Sentiment Score, Sentiment Rule Modifiers, Long-Form Sentiment Analysis.

What are some open-source alternatives to cjhutto/vadersentiment?

Open-source alternatives to cjhutto/vadersentiment include: alfred1984/interesting-python — This project is a collection of Python implementations for web scraping, network traffic interception, data analysis,… dongrixinyu/jionlp — JioNLP is a Chinese natural language processing toolkit designed for cleaning, normalizing, and extracting structured… axa-group/nlp.js — nlp.js is a JavaScript natural language processing library and development framework used to build natural language… hit-scir/ltp — This is a Chinese natural language processing toolkit providing a suite of tools for word segmentation, part-of-speech… johnsnowlabs/spark-nlp — Spark NLP is a toolkit for scalable text analysis and machine learning built on the Apache Spark distributed computing… isnowfy/snownlp — SnowNLP is a Python library for Chinese natural language processing. It provides tools for text segmentation,…