vader-sentiment-analysis

Use NLTK's SentimentIntensityAnalyzer to compute polarity_scores on each text, producing positive, negative, neutral, and compound values. Preprocess by removing URLs, mentions, and HTML while preserving capitalization, punctuation, and emoticons that VADER uses as intensity signals.

The compound score is a normalized valence sum bounded to [-1, +1] using the formula sum(V_i) / sqrt(sum(V_i)^2 + 15). Texts scoring at or above 0.05 are classified positive, at or below -0.05 negative, and between them neutral, though thresholds should be calibrated per corpus.

No, lowercasing destroys capitalization signals that VADER uses for intensity boosting, worth roughly 0.733 valence per ALL CAPS word. Pass original-case text and only remove noise like URLs, mentions, and HTML tags.

Avoid VADER for formal, technical, legal, or financial text, non-English content, and corpora dominated by sarcasm or irony, which it systematically misreads. It also cannot provide aspect-level sentiment toward specific entities or serve as evidence of emotional states.

Resample per-text compound scores into temporal windows (daily, weekly, or monthly depending on corpus span) with minimum text counts per window, then compute rolling smoothed means. Detect patterns like volatility spikes, inflections, and polarity reversals from the windowed trajectory.

VADER is lexicon-based and reads literal word valence without understanding context, so a phrase like 'Oh great, another failure' scores positive. Flag sarcasm-prone corpora in the report limitations and cross-validate with a transformer model or manual review.