[自然语言处理]文本主题相关(TF-IDF/LDA/Sentence-BERT)

1 TF-IDF

常用于挖掘文本关键词：

• TF(词频) = 词在本文的出现次数/文章的总词数
• IDF(逆文档频率) = log(语料库的文档总数/包含该词的文档数+1)
• 对于某一篇文章，计算出文档的每个词的TF-IDF值，然后按降序排列，取排在最前面的几个词，即为本文关键词

import jieba
import numpy as np

texts=[
    '...',
    '...',
    '...',
    '...'
]

# sklearn
# 对于中文文档，需要提前使用jieba进行分词
x_train = [" ".join(jieba.cut(text)) for text in texts[:3]]
x_test = [" ".join(jieba.cut(text)) for text in texts[3:]]

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer

vectorizer = CountVectorizer()
tf_idf_transformer = TfidfTransformer()

x_train_tf_idf = tf_idf_transformer.fit_transform(vectorizer.fit_transform(x_train))
x_test_tf_idf = tf_idf_transformer.transform(vectorizer.transform(x_test))

x_train_weight = x_train_tf_idf.toarray()
x_test_weight = x_test_tf_idf.toarray()  
words = vectorizer.get_feature_names()

for w in x_train_weight:
    loc = np.argsort(-w)
    for i in range(5):
        print('{}: {} {}'.format(str(i + 1), words[loc[i]], w[loc[i]]))
    print('\n')

# gensim
x_train = [jieba.lcut(text) for text in texts[:3]]
x_test = [jieba.lcut(text) for text in texts[3:]]

from gensim import corpora
from gensim import models

# 建立词表
dic = corpora.Dictionary(x_train)
# 建立id2count
x_train_bow = [dic.doc2bow(sentence) for sentence in x_train]

tfidf = models.TfidfModel(x_train_bow)
tfidf_vec = []
for sentence in x_test:
    word_bow = dic.doc2bow(sentence.lower())
    word_tfidf = tfidf[word_bow]
    tfidf_vec.append(word_tfidf)
# 输出 词语id与词语tfidf值
print(tfidf_vec)

# jieba
import jieba.analyse

# idf使用jieba默认的，也可以自行指定
keywords = jieba.analyse.extract_tags(texts[0], topK=5)

对于大规模文本，还可使用spark实现tf-idf：Spark MLlib TF-IDF – Example

2 LDA

常用于本文主题分析。