High-accuracy polarity classification¶

The frozen polarity lexicon is great for inspecting morpheme-level sentiment, but for classifying whole texts as positive or negative — especially text from a different domain than KOSAC — two additions in v0.5.0 do much better: multi-scale scoring (the default) and the derived polarity-blend lexicon.

The quick path¶

from kosac import SentimentAnalyzer

clf = SentimentAnalyzer("polarity-blend")     # multi-scale scoring by default

clf.predict_polarity("이 영화 정말 재미있고 최고였다")   # 'POS'
clf.predict_polarity("시간 낭비 최악의 영화 너무 지루하다")  # 'NEG'
clf.predict_polarity_batch(["좋았다", "별로였다"])        # ['POS', 'NEG']

For a continuous score (e.g. to set your own threshold or rank by sentiment):

clf.polarity_score("이 영화 정말 재미있다")   #  0.97   (P(POS) − P(NEG), in [-1, 1])
clf.polarity_score("시간 낭비 최악의 영화")    # -1.00

predict_polarity(text, threshold=0.0) labels 'POS' when the score exceeds the threshold. Raise the threshold to trade recall for precision — useful when the negative class is rare.

Why it works¶

`polarity-blend`¶

polarity-blend is a derived POS/NEG lexicon: the frozen KOSAC seeds combined with a lexicon learned from the NSMC movie-review corpus. The KOSAC seeds alone are near-chance out of domain; blending in corpus-learned signal makes it transfer (on the NIKL out-of-domain benchmark, balanced accuracy rises from ≈0.53 to ≈0.73).

It is loadable by name but is intentionally not one of the six canonical kosac.FEATURES, so SentimentAnalyzer("all") is unaffected.

import kosac
lex = kosac.load_lexicon("polarity-blend", ngrams=[1, 2, 3])
lex.get_labels()    # ['NEG', 'POS']

Multi-scale scoring¶

The legacy matcher was greedy leftmost-longest and non-overlapping: when a trigram matched, its unigrams were skipped. Multi-scale scoring instead sums every overlapping n-gram match, so short and long evidence both count. It is the default; the old behavior is one argument away:

SentimentAnalyzer("polarity-blend", scoring="greedy")             # legacy matcher
SentimentAnalyzer("polarity-blend", ngram_weights={1: 1, 2: 1, 3: 1})  # weight scales

(count() always stays non-overlapping — double-counting would distort word tallies. Multi-scale applies to analyze() / polarity_score().)

Regenerating or strengthening the blend¶

The shipped blend is a compact version. Rebuild it — or build the larger “champion” variant that scores a little higher — from source (needs the [kiwi] extra; downloads NSMC, CC0):

python -m benchmarks.build_shipped_blend          # the shipped (compact) blend
python -m benchmarks.build_shipped_blend --full   # the larger champion blend

The full benchmark campaign — datasets, decision rules, and every ablation — is documented in benchmarks/README.md.

License note¶

polarity-blend is derived data under CC BY-SA 4.0 (it mixes the CC BY-SA KOSAC seeds with the CC0 NSMC corpus). See src/kosac/data/polarity-blend.NOTICE.