自然语言处理库——TextBlob
姬实
TextBlob（https://textblob.readthedocs.io/en/dev/index.html）是一个用于处理文本数据的Python库。它提供一个简单的API，可用于深入研究常见的NLP任务，如词性标注、名词短语提取、情感分析、文本翻译、分类等。
官方文档：https://textblob.readthedocs.io/en/dev/
目录
1. 情感分析
情感指的是隐藏在句子中的观点，极性（polarity）定义句子中的消极性或积极性，主观性（subjectivity）暗示句子的表达的含糊的、还是肯定的。
返回一个元组 Sentiment(polarity, subjectivity).
polarity： [-1.0, 1.0]. -1.0 消极，1.0积极
subjectivity： [0.0, 1.0] 0.0 表示客观，1.0表示主观.
from textblob import TextBlob
text = "Textblob is amazingly simple to use. What great fun!"
blob = TextBlob(text) # 创建一个textblob对象
from textblob import TextBlob
result = blob.sentiment
# Sentiment(polarity=0.39166666666666666, subjectivity=0.4357142857142857)
polarity = blob.sentiment.polarity # 0.391666666666666662.词性标注
wiki = TextBlob("Python is a high-level, general-purpose programming language.")
tag = wiki.tags
# [('Python', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('high-level', 'JJ'), ('general-purpose', 'JJ'), ('programming', 'NN'), ('language', 'NN')]
3. 分词和分句
blob = TextBlob("Beautiful is better than ugly. "
"Explicit is better than implicit. "
"Simple is better than complex.")
word = blob.words
sentence = blob.sentences
'''
['Beautiful', 'is', 'better', 'than', 'ugly', 'Explicit', 'is', 'better', 'than', 'implicit', 'Simple', 'is', 'better', 'than', 'complex']
[Sentence("Beautiful is better than ugly."),
Sentence("Explicit is better than implicit."),
Sentence("Simple is better than complex.")]
'''4. 名词短语列表
list = wiki.noun_phrases
# ['python']5. 词形还原及词干提取
（1）单复数
singularize() 变单数， pluralize（）变复数，用在对名词进行处理，且会考虑特殊名词单复数形式
sentence = TextBlob('Use 4 spaces per indentation level.')
word = sentence.words
danshu = word[2].singularize() # space
fushu = word[-1].pluralize() # levels（2）Word 类
lemmatize() 方法 对单词进行词形还原，名词找单数，动词找原型。所以需要一次处理名词，一次处理动词。
from textblob import Word
w1 = Word('apples')
result1 = w1.lemmatize() # 默认只处理名词 apple
w2 = Word('went')
result2 = w2.lemmatize("v") # 对动词原型处理 go（3）WordNet：获取近义词
# 1.获取近义词
from textblob import Word
from textblob.wordnet import VERB
result1 = Word("hack").synsets
result2 = Word("hack").get_synsets(pos=VERB)
#get_synsets()： 只查找 该词作为 动词 的集合，参数为空时和synsets方法相同
'''
result1：[Synset('hack.n.01'), Synset('machine_politician.n.01'), Synset('hack.n.03'),
Synset('hack.n.04'), Synset('cab.n.03'), Synset('hack.n.06'), Synset('hack.n.07'),
Synset('hack.n.08'), Synset('chop.v.05'), Synset('hack.v.02'), Synset('hack.v.03'),
Synset('hack.v.04'), Synset('hack.v.05'), Synset('hack.v.06'), Synset('hack.v.07'), Synset('hack.v.08')]
result2：[Synset('chop.v.05'), Synset('hack.v.02'), Synset('hack.v.03'), Synset('hack.v.04'),
Synset('hack.v.05'), Synset('hack.v.06'), Synset('hack.v.07'), Synset('hack.v.08')]
'''
2. 获取近义词的定义
defi = result1[1].definition() # 获取定义
#defi结果： a politician who belongs to a small clique that controls a political party for private rather than public ends
3. 获取单词本身的定义
defi = Word("octopus").definitions
# ['tentacles of octopus prepared as food', 'bottom-living cephalopod having a soft oval body with eight long tentacles']6. 拼写矫正
（1）直接矫正
b = TextBlob("I havv goood speling!")
b_corr = b.correct()
print(b_corr) # I have good spelling!（2）Word 拼写检查
word.spellcheck()方法，返回带有拼写建议的（word，confidence）元组列表
from textblob import Word
w = Word('falibility')
w_ = w.spellcheck()
print(w_) # [('fallibility', 1.0)]7. 单词词频
（1）单词词频
monty = TextBlob("We are no longer the Knights who say Ni. "
"We are now the Knights who say Ekki ekki ekki PTANG.")
#（1）方式1
counts = monty.word_counts['ekki'] # 不区分大小写
print(counts) # 3
#（2）方式2
counts2 = monty.words.count('ekki')
print(counts2) # 3
#（3）方式3
counts3 = monty.words.count('ekki', case_sensitive=True) # 设置大小写敏感，默认不区分
print(counts3) # 2（2）短语频次
counts4 = wiki.noun_phrases.count('python') # 短语频次
print(counts4) # 18. 翻译及语言检测语言
en_blob = TextBlob('Simple is better than complex.')
lang = en_blob.translate(to='es') # from_lang默认 en
print(lang)
# TextBlob("Simple es mejor que complejo.")
chinese_blob = TextBlob("美丽优于丑陋")
lang = chinese_blob.translate(from_lang="zh-CN", to='en')
print(lang)
# TextBlob("Beautiful is better than ugly")
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