Case study: a 3-class lexicon from 모두의 말뭉치 (NIKL) expressions¶

The NSMC case study builds a binary lexicon from documents labeled positive / negative. Many sentiment corpora instead label short expressions (phrases) with a numeric polarity score. A common shape — e.g. a 모두의 말뭉치 (NIKL) sentiment-analysis export such as EXSA2002108040.csv — is a data frame with an expression_form column (the phrase) and an integer expression_score in −2..2:

expression_form	expression_score
정말 마음에 쏙 들어요	2
생각보다 괜찮네요	1
그냥 보통이에요	0
조금 아쉬워요	-1
완전 실망했어요	-2

Note

The rows above are invented for illustration. The expression text in a real corpus may be licensed or private — keep it on your machine; nothing below needs to publish it, only the derived morpheme lexicon.

The same custom-lexicon machinery turns this into a morpheme polarity lexicon — the only new step is collapsing the −2..2 score into three classes, giving a POS / NEUT / NEG lexicon (KOSAC’s own polarity labels minus COMP/None). examples/nikl_lexicon.py does all of this as a runnable script.

Mapping score → label¶

Corpus reads a headerless text,label CSV, so map the score to a class and write the two columns. Positive scores are POS, negative NEG, zero NEUT:

import pandas as pd

def score_to_label(score):
    if score > 0:
        return 'POS'
    if score < 0:
        return 'NEG'
    return 'NEUT'

df = pd.read_csv('EXSA2002108040.csv')        # your 모두의 말뭉치 export
df['expression_form'] = df['expression_form'].astype(str).str.strip()
df = df[df['expression_form'] != '']
df['label'] = df['expression_score'].astype(int).map(score_to_label)
df[['expression_form', 'label']].to_csv('expr_corpus.csv', index=False, header=False)

Note

Real expression corpora are usually class-imbalanced — review data skews positive (here ≈ 16k POS / 2.7k NEG / 0.8k NEUT across product, movie, and travel reviews). That imbalance carries into the lexicon and biases sentence scores toward the majority class; see the caveat at the end.

Building¶

update_from_corpus tokenizes each expression into morpheme N-grams and counts its label for every N-gram. Declare the three labels, restrict to content words with pos_tag, and drop rare entries with min_freq:

from kosac.corpora import Corpus
from kosac.lexicon import GenericLexicon
from kosac.tokenizers import KiwiTokenizer

corpus = Corpus('expr_corpus.csv')
corpus.get_labels()        # the three classes (order follows first appearance)

lex = GenericLexicon(ngrams=[1, 2, 3])
lex.set_labels(['POS', 'NEUT', 'NEG'])
lex.update_from_corpus(
    corpus, KiwiTokenizer(),
    pos_tag={'NNG', 'NNP', 'VV', 'VA', 'XR', 'MAG'},   # content words
    min_freq=5,
)
lex.get_size()             # entries seen >= 5 times (uni + bi + tri-grams)

Inspecting¶

Sort the content unigrams by dominant-label proportion (max.prop) for a quick sanity check — the strongly-polar morphemes should land in the expected column:

df = lex.get_lexicon()
uni = df[(df['ngram'] == 1) & (df['freq'] >= 10)]
uni[uni['max.value'] == 'POS'].sort_values('max.prop', ascending=False).head(10)

The columns separate by kind of word. The entries below are the actual unigrams from this build — a mixed-domain review corpus (product, movie, and travel reviews), so the strongest-polar words carry a domain flavour: skincare terms from the product reviews (촉촉, 흡수, 제형), film terms from the movie reviews (유치, 뻔하):

POS	NEUT	NEG
촉촉 (moist), 편하 (comfortable), 굿 (good)	호불호 (hit-or-miss), 궁금 (curious)	씁쓸 (bitter), 어설프 (clumsy), 싫 (dislike)
안심 (reassuring), 진정 (soothing), 흡수 (absorbs well)	묽 (watery), 비슷 (similar), 제형 (texture)	아쉬움 (let-down), 유치 (cheesy), 뻔하 (predictable)

NEUT is the smallest and noisiest class — score-0 expressions are sparse, so its top entries are mixed or factual words (호불호 “love-it-or-hate-it”, 묽 “watery”, 제형 “texture”) rather than strong sentiment; relax the freq filter to surface them. Bigrams add light constructions the unigrams miss (도/JX 좋/VA “… is also good”).

Scoring¶

A custom lexicon supports the same matching and scoring as the bundled ones — get_sent_probs is a softmax over the matched morphemes’ add-one-smoothed log-probabilities:

tok = KiwiTokenizer()
lex.get_sent_probs('이 제품 정말 좋고 만족스러워요', tok).round(3).to_dict()
# {'POS': 0.997, 'NEUT': 0.002, 'NEG': 0.002}
lex.get_sent_probs('완전 실망했고 최악이에요', tok).round(3).to_dict()
# {'NEG': 0.557, 'POS': 0.437, 'NEUT': 0.006}

A sentence with no matching entry has no evidence, so the distribution is uniform rather than an error:

lex.get_sent_probs('xyz', tok).to_dict()
# {'POS': 0.333, 'NEUT': 0.333, 'NEG': 0.333}

Warning

Notice the second example only just lands on NEG (0.557). With a POS-skewed corpus, weakly-negative sentences can come out POS: every smoothed entry leaks a little probability to the majority class, and enough neutral-ish matches outvote one negative word. If balanced predictions matter more than reproducing the corpus distribution, tighten the match set (set_lexicon(min_freq=…, threshold=…) to keep only high-max.prop entries), down-sample the majority class before building, or compare summed log-probs only between POS/NEG.

Saving¶

save() writes the package’s absolute-count CSV (ngram + one column per label); the constructor reads it back and a GenericLexicon infers the labels from the columns:

lex.save('nikl_lexicon.csv')

reloaded = GenericLexicon(filepath='nikl_lexicon.csv', ngrams=[1, 2, 3])
reloaded.get_labels()        # ['POS', 'NEUT', 'NEG']  (inferred from the columns)

The saved CSV holds only the derived morpheme counts, not the source expressions — so it is safe to share even when the original corpus text is not.

Note

This lexicon isn’t bundled with the package — you build it from your own expression-scored data with the snippets above (or examples/nikl_lexicon.py, which falls back to a tiny built-in sample of invented phrases so it runs with no corpus: python examples/nikl_lexicon.py --data EXSA2002108040.csv). The package ships only the frozen KOSAC lexicons.

References¶

National Institute of Korean Language (2021). NIKL Sentiment Analysis Corpus (v.1.0). URL: https://kli.korean.go.kr/corpus