nlp分词之TextBlob
TextBlob
TextBlob是用于处理文本数据的Python（2和3）库。它提供了一个一致的API，可用于深入研究普通自然语言处理（NLP）任务，例如词性标记，名词短语提取，情感分析等。
主要用于英文的分词，不适用于中文
安装TextBlob
可以在PyCharm开发工具中Python Console窗口用pip install textblob
词性标注
from textblob import TextBlob
wiki = TextBlob("Python is a high-level, general-purpose programming language.")
print(wiki.tags)
输出：
[('Python', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('high-level', 'JJ'),
('general-purpose', 'JJ'), ('programming', 'NN'), ('language', 'NN')]
名词短语提取
from textblob import TextBlob
wiki = TextBlob("Python is a high-level, general-purpose programming language.")
print(wiki.tags)
输出：
['python']
情感分析
polarity：情感积极消极在[-1,1]之间，越接近-1越消极，越接近1越积极
subjectivity：主观客观在[0,1]之间，越接近1越主观
from textblob import TextBlob
testimonial = TextBlob("Textblob is amazingly simple to use. What great fun!")
print(testimonial.sentiment)
输出：
Sentiment(polarity=0.39166666666666666, subjectivity=0.4357142857142857)
符号化
分词，分句
text = "Beautiful is better than ugly. "\
" Explicit is better than implicit. "\
"Simple is better than complex. "\
# 利用textblob实现分句
blob = TextBlob(text)
sentences = blob.sentences
print("1分句：",sentences)
words_list = [] #声明一个list存储所有的分词结果
for sentence in sentences:
words_list.append(sentence.words)
print(sentence.words)
print("2 分词：",words_list)
1分句： [Sentence("Beautiful is better than ugly."), Sentence("Explicit is better than implicit."), Sentence("Simple is better than complex.")]
['Beautiful', 'is', 'better', 'than', 'ugly']
['Explicit', 'is', 'better', 'than', 'implicit']
['Simple', 'is', 'better', 'than', 'complex']
2 分词： [WordList(['Beautiful', 'is', 'better', 'than', 'ugly']), WordList(['Explicit', 'is', 'better', 'than', 'implicit']), WordList(['Simple', 'is', 'better', 'than', 'complex'])]
拼写校正
b=TextBlob("I havv goood speling!")
print(b.correct())
输出：
I have good spelling!
获取单词和名词短语频率
两种方法获取单词中名词或名词短语的出现频率
1、通过word_counts字典
2、通过count()
monty = TextBlob("We are no longer the Knights who say Ni. We are now the Knights who say Ekki ekki ekki PTANG.")
print("ekki:",monty.word_counts['ekki'])
print("ekki:",monty.words.count('ekki'))
#是否区分大小写
print("ekki_sensitive:",monty.words.count('ekki',case_sensitive=True))
输出：
ekki: 3
ekki: 3
ekki_sensitive: 2
机器翻译
这个需要，演示不了了
en_text = "I think KFC is not good. "
en_blob = textblob.TextBlob(en_text)
zh_text = en_blob.translate(from_lang='en',to='zh-CN')