Add VADER sentiment analysis tool

- Rule-based sentiment analysis optimized for social media text
- No language models required - uses bundled lexicon
- Supports JSON and tabular output formats
- Includes comprehensive test data and documentation

Co-Authored-By: Claude Sonnet 4 <noreply@anthropic.com>

Author: ksuderman

tools/vader/.shed.yml

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+name: vader_sentiment
+owner: iuc
+description: VADER sentiment analysis for text
+long_description: |
+  Performs sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner),
+  a rule-based model optimized for social media and general text. Works well on political
+  speeches, news articles, and opinion text. No language models required.
+homepage_url: https://github.com/cjhutto/vaderSentiment
+remote_repository_url: https://github.com/ksuderman/galaxy_tools_vader
+type: unrestricted
+categories:
+  - Text Manipulation
+  - Natural Language Processing

tools/vader/README.md

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+# Galaxy Wrapper for VADER Sentiment Analysis
+
+This Galaxy tool performs sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner), a lexicon and rule-based sentiment analysis tool specifically designed for social media and informal text.
+
+## Features
+
+- **Social media optimized**: Handles slang, emoticons, punctuation emphasis, and informal language
+- **No dependencies**: Bundled lexicon with no external model downloads required
+- **Fast processing**: Rule-based approach for high-speed sentiment analysis
+- **Interpretable scores**: Clear numerical scores with rule-based explanations
+- **Flexible granularity**: Per-sentence or whole-document analysis
+- **Dual output formats**: TSV for analysis and JSON for programmatic use
+
+## Requirements
+
+- **Input**: Plain text files
+- **No dependencies**: Pure Python implementation with bundled VADER lexicon
+- **No setup required**: Ready to use immediately after installation
+
+## When to Use VADER
+
+VADER excels at analyzing:
+
+| Text Type | Why VADER Works Well |
+|---|---|
+| **Political speeches** | Handles rhetorical language, emphasis, and persuasive tone |
+| **News articles** | Captures positive/negative framing and editorial stance |
+| **Social media posts** | Originally designed for Twitter, Facebook, and informal text |
+| **Opinion pieces** | Effective on subjective, evaluative text |
+| **Short informal text** | Handles slang, emoticons, and unconventional punctuation |
+
+## VADER vs. Model-Based Approaches
+
+Choose VADER over neural sentiment models (spaCy, Stanza) when:
+
+- ✅ **Interpretability**: Need rule-based, explainable sentiment scores
+- ✅ **Speed**: Processing large volumes of text quickly
+- ✅ **No models**: Don't want to download large language models
+- ✅ **Social media**: Analyzing informal, social media-style text
+- ✅ **Lightweight**: Minimal computational requirements
+
+Choose neural models for:
+- 📚 **Formal text**: Academic papers, literature, formal documents
+- 🎭 **Complex sentiment**: Subtle, contextual, or sarcastic expressions
+- 🌍 **Multilingual**: Non-English text analysis
+
+## Sentiment Scores
+
+VADER produces four scores for each text unit:
+
+### Compound Score (-1 to +1)
+**Primary metric**: Overall sentiment intensity
+- `≥ +0.05`: **Positive** sentiment
+- `≤ -0.05`: **Negative** sentiment  
+- `-0.05 to +0.05`: **Neutral** sentiment
+
+### Component Scores (0 to 1)
+- **Positive**: Proportion of positive sentiment
+- **Negative**: Proportion of negative sentiment
+- **Neutral**: Proportion of neutral sentiment
+
+*Note: Positive + Negative + Neutral = 1.0*
+
+## Analysis Granularity
+
+### Per Sentence (Default)
+- Splits text into sentences automatically
+- Scores each sentence individually
+- Produces table with one row per sentence
+- Best for: Tracking sentiment changes throughout a document
+
+### Whole Document
+- Scores entire text as single unit
+- Single sentiment score for the complete document  
+- Best for: Overall document sentiment classification
+
+## Output Formats
+
+### Tabular (TSV)
+Tab-separated file with columns:
+- `sentence`: Text of the analyzed unit
+- `compound`: Overall sentiment score (-1 to +1)
+- `positive`: Positive proportion (0 to 1)
+- `negative`: Negative proportion (0 to 1)
+- `neutral`: Neutral proportion (0 to 1)
+- `label`: Classification (positive/negative/neutral)
+
+### JSON
+Structured JSON format:
+```json
+[
+  {
+    "text": "This movie is great!",
+    "compound": 0.6249,
+    "positive": 0.779,
+    "negative": 0.0,
+    "neutral": 0.221,
+    "label": "positive"
+  }
+]
+```
+
+## Example Use Cases
+
+- **Political analysis**: Sentiment tracking in campaign speeches and debates
+- **Media monitoring**: News sentiment analysis and bias detection
+- **Social media research**: Public opinion analysis on Twitter/Facebook
+- **Customer feedback**: Product review sentiment classification
+- **Historical text analysis**: Sentiment trends in historical documents
+- **Content moderation**: Identifying negative sentiment in user-generated content
+
+## VADER's Strengths
+
+- **Punctuation awareness**: "Good!!!" is more positive than "Good"
+- **Capitalization**: "AMAZING" is stronger than "amazing"
+- **Degree modifiers**: "very good" vs "slightly good"
+- **Conjunction handling**: "but" and "however" shift sentiment
+- **Emoticon support**: :) :( :D are properly interpreted
+- **Slang recognition**: Modern informal expressions
+
+## Installation
+
+Install this tool from the Galaxy Toolshed: `vader_sentiment`
+
+No additional setup required - the VADER lexicon is bundled with the tool.
+
+## Citation
+
+If you use this tool, please cite the original VADER paper:
+
+```
+Hutto, C.J. & Gilbert, Eric. (2014). VADER: A Parsimonious Rule-based Model for 
+Sentiment Analysis of Social Media Text. Proceedings of the Eighth International 
+AAAI Conference on Weblogs and Social Media.
+```
+
+## License
+
+VADER is released under the MIT License, allowing unrestricted use in research and commercial applications.
+
+## Version History
+
+- **3.3.2+galaxy0**: Initial Galaxy release with bundled lexicon and dual output formats