對文本進行情感分析：TextBlob

TextBlob是一個用 Python (2和3)編寫的開源的文本處理庫。它可以用來執行很多自然語言處理的任務，比如，詞性標注，名詞性成分提取，情感分析，文本翻譯，等等。你可以在官方文檔閱讀TextBlog的所有特性。

為什么我要關心TextBlob？

我學習TextBlob的原因如下：

1. 我想開發需要進行文本處理的應用。我們給應用添加文本處理功能之后，應用能更好地理解人們的行為，因而顯得更加人性化。文本處理很難做對。TextBlob站在巨人的肩膀上（NTLK），NLTK是創建處理自然語言的Python程序的最佳選擇。

2. 我想學習下如何用 Python 進行文本處理。

from textblob import TextBlob

text = '''
The titular threat of The Blob has always struck me as the ultimate movie
monster: an insatiably hungry, amoeba-like mass able to penetrate
virtually any safeguard, capable of--as a doomed doctor chillingly
describes it--"assimilating flesh on contact.
Snide comparisons to gelatin be damned, it's a concept with the most
devastating of potential consequences, not unlike the grey goo scenario
proposed by technological theorists fearful of
artificial intelligence run rampant.
'''

blob = TextBlob(text)
blob.tags           # [(u'The', u'DT'), (u'titular', u'JJ'),
                    #  (u'threat', u'NN'), (u'of', u'IN'), ...]

blob.noun_phrases   # WordList(['titular threat', 'blob',
                    #            'ultimate movie monster',
                    #            'amoeba-like mass', ...])

for sentence in blob.sentences:
    print(sentence.sentiment.polarity)
# 0.060
# -0.341

blob.translate(to="es")  # 'La amenaza titular de The Blob...'

特性

• Noun phrase extraction
• Part-of-speech tagging
• Sentiment analysis
• Classification (Naive Bayes, Decision Tree)
• Language translation and detection powered by Google Translate
• Tokenization (splitting text into words and sentences)
• Word and phrase frequencies
• Parsing
• n-grams
• Word inflection (pluralization and singularization) and lemmatization
• Spelling correction
• Add new models or languages through extensions
• WordNet integration

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