TopolearnVADER gives a solid, training-free sentiment baseline for reviews and social media in a few lines.
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a rule-based sentiment model tuned for social media. It needs the vader_lexicon data package. Create SentimentIntensityAnalyzer() and call polarity_scores(text) to get a dict:
neg,neu,pos: the proportions of the text that fall in each class (they sum to about 1)compound: a normalised overall score from -1 (most negative) to +1 (most positive)
The usual thresholds: compound >= 0.05 is positive, <= -0.05 negative, otherwise neutral.
VADER is more than a word list. Its rules handle:
- negation: "not good" flips polarity
- intensifiers: "very good" > "good"; "kind of good" < "good"
- capitalisation and punctuation: "GOOD!!!" scores higher than "good"
- "but": the clause after "but" weighs more
- emoticons, common slang and emoji descriptions
Feed it raw text, not stemmed or lowercased tokens, because case, punctuation and stopwords like "not" carry signal.
Limitations: it doesn't understand sarcasm, domain-specific terms ("unpredictable" is good for a thriller, bad for a car) or long documents well. Score per sentence and aggregate for longer texts, and validate against labelled examples from your domain before trusting it; a trained classifier or transformer model usually beats it when labels exist.
Example
from nltk.sentiment import SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()
def label(text):
c = sia.polarity_scores(text)["compound"]
return "pos" if c >= 0.05 else "neg" if c <= -0.05 else "neu"
print(sia.polarity_scores("The food was good."))
# {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}
for t in ["The food was good.", "The food was not good.",
"The food was VERY GOOD!!!", "The food was good, but the service was awful.",
"The table is made of wood."]:
print(f"{sia.polarity_scores(t)['compound']:+.3f} {label(t)} {t}")
# +0.440 pos The food was good.
# -0.341 neg The food was not good.
# +0.760 pos The food was VERY GOOD!!!
# -0.468 neg The food was good, but the service was awful.
# +0.000 neu The table is made of wood.Remember
- polarity_scores returns neg/neu/pos proportions and a compound score
- compound >= 0.05 positive, <= -0.05 negative is the usual rule
- Rules handle negation, intensifiers, caps, punctuation and 'but'
- Give it raw text and validate on your own domain
Watch out for
- Lowercasing and stripping punctuation before VADER, which throws away intensity cues.