[AI SCHOOL 5기] 텍스트 분석 실습 - 텍스트 분석

2022. 3. 25. 19:00

NLTK Library #

NLTK(Natural Language Toolkit)은 자연어 처리를 위한 라이브러리

python

import nltk

nltk.download()

문장을 단어 수준에서 토큰화 #

python

sentence = 'NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.'

nltk.word_tokenize(sentence)

Output

bash

['NLTK',
 'is',
 'a',
 'leading',
 'platform',
 ...

POS Tagging #

python

tokens = nltk.word_tokenize(sentence)
nltk.pos_tag(tokens)

Output

bash

[('NLTK', 'NNP'),
 ('is', 'VBZ'),
 ('a', 'DT'),
 ('leading', 'VBG'),
 ('platform', 'NN'),
 ...

NLTK POS Tags List #

nltk-pos-tags-1

nltk-pos-tags-2

Stopwords 제거 #

python

from nltk.corpus import stopwords

stop_words = stopwords.words('english')
stop_words.append(',')
stop_words.append('.')

result = []

for token in tokens:
    if token.lower() not in stopWords:
        result.append(token)

Output

python

['NLTK', 'leading', 'platform', 'building', 'Python', 'programs', 'work', 'human', 'language', 'data', 'provides', 'easy-to-use', 'interfaces', '50', 'corpora', 'lexical', 'resources', 'WordNet', 'along', 'suite', 'text', 'processing', 'libraries', 'classification', 'tokenization', 'stemming', 'tagging', 'parsing', 'semantic', 'reasoning', 'wrappers', 'industrial-strength', 'NLP', 'libraries', 'active', 'discussion', 'forum']

Lemmatizing #

Lemmatization: 단어의 형태소적/사전적 분석을 통해 파생적 의미를 제거하고,
어근에 기반하여 기본 사전형인 lemma를 찾는 것

python

lemmatizer = nltk.wordnet.WordNetLemmatizer()

print(lemmatizer.lemmatize("cats"))             # cat
print(lemmatizer.lemmatize("geese"))            # goose

print(lemmatizer.lemmatize("better"))           # better
print(lemmatizer.lemmatize("better", pos="a"))  # good

print(lemmatizer.lemmatize("ran"))              # ran
print(lemmatizer.lemmatize("ran", 'v'))         # run

default로 n 이므로 ‘cats’, ‘geese’ 들은 기본명사형을 반환
형용사 ‘better’는 pos에 a를 함께 입력해주어야 원형인 ‘good’을 반환
동사 ‘ran’은 pos에 v를 함께 입력해주어야 원형인 ‘run’을 반환

영화 리뷰 데이터 전처리 #

python

file = open('moviereview.txt', 'r', encoding='utf-8')
lines = file.readlines()

sentence = lines[1]
tokens = nltk.word_tokenize(sentence)

lemmas = []
for token in tokens:
    if token.lower() not in stop_words:
        lemmas.append(lemmatizer.lemmatize(token))