"algo-social-sentiment"

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

1. Look up each token in VADER lexicon (7,500+ sentiment-rated terms)
2. Apply grammatical rules: negation ("not good" = negative), degree modifiers ("very good" > "good"), capitalization boost, punctuation amplification
3. Compute raw valence scores for positive, negative, neutral proportions
4. 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