Algo Social Sentiment Agent Skill

VADER Sentiment Analysis

Overview

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment tool optimized for social media. Returns compound score [-1, +1] combining positive, negative, and neutral proportions. Runs in O(n) per text where n = word count. No training required.

When to Use

Trigger conditions:

• Analyzing sentiment in social media posts, tweets, or reviews
• Quick sentiment scoring without ML model training
• Processing text with slang, emoticons, and informal language

When NOT to use:

• For formal/academic text (VADER is tuned for social media)
• When domain-specific sentiment matters (e.g., financial sentiment — use FinBERT)
• When sarcasm detection is critical (VADER doesn't detect sarcasm)

Algorithm

IRON LAW: VADER Is Designed for SOCIAL MEDIA Text
It handles slang, emoticons, capitalization, and punctuation as
sentiment modifiers. Applying it to formal documents (legal, academic,
medical) produces unreliable scores. For domain-specific text, use
domain-trained models instead.

Phase 1: Input Validation

Tokenize text. Preserve: capitalization (ALL CAPS = emphasis), punctuation (! amplifies), emoticons/emoji.

Gate: Text is non-empty, encoding handled correctly.

Phase 2: Core Algorithm

• Look up each token in VADER lexicon (7,500+ sentiment-rated terms)
• Apply grammatical rules: negation ("not good" = negative), degree modifiers ("very good" > "good"), capitalization boost, punctuation amplification
• Compute raw valence scores for positive, negative, neutral proportions
• Compute compound score: normalized sum of all valence scores using formula: compound = sum / √(sum² + α) where α = 15

Phase 3: Verification

Classify: compound ≥ 0.05 → positive, ≤ -0.05 → negative, else neutral. Spot-check sample results.

Gate: Classifications pass manual spot-check on 10-20 examples.

Phase 4: Output

Return compound score and polarity classification per text.

Output Format

{
  "results": [{"text": "...", "compound": 0.76, "pos": 0.45, "neu": 0.55, "neg": 0.0, "label": "positive"}],
  "metadata": {"texts_analyzed": 500, "distribution": {"positive": 0.45, "neutral": 0.35, "negative": 0.20}}
}

Examples

Sample I/O

Input: "This product is AMAZING!!! 😍"

Expected: compound ≈ 0.87 (positive). Boosted by: CAPS, !!!, 😍 emoji.

Edge Cases

| Input | Expected | Why |

|-------|----------|-----|

| "Not bad at all" | Slightly positive (~0.2) | Double negation partially handled |

| "😂😂😂" | Positive | Emoji mapped in lexicon |

| Empty string | Compound = 0, neutral | No tokens to score |

Gotchas

• Sarcasm is invisible: "Oh great, another meeting" reads as positive. VADER has no sarcasm detection.
• Negation window: VADER applies negation within a 3-word window. "I do not think this is bad" may misparse the negation chain.
• Emoji coverage: VADER's emoji lexicon may not cover newer emoji. Update or supplement as needed.
• Language limitation: VADER is English-only. For Chinese/Japanese, use language-specific tools (e.g., SnowNLP for Chinese).
• Compound threshold sensitivity: The 0.05 boundary is arbitrary. Adjust thresholds based on your specific use case and tolerance for false positives.

References

• For VADER lexicon and rules documentation, see references/vader-rules.md
• For comparison with transformer-based sentiment models, see references/model-comparison.md