Sentiment Classifier

In [7]:

    

! pip install -U textblob


    

    




Collecting textblob
  Downloading textblob-0.15.1-py2.py3-none-any.whl (631kB)
    100% |████████████████████████████████| 634kB 2.0MB/s ta 0:00:01
Collecting nltk>=3.1 (from textblob)
  Downloading nltk-3.2.5.tar.gz (1.2MB)
    100% |████████████████████████████████| 1.2MB 1.2MB/s ta 0:00:011
Requirement already up-to-date: six in /Users/falehalrashidi/anaconda3/lib/python3.6/site-packages (from nltk>=3.1->textblob)
Building wheels for collected packages: nltk
  Running setup.py bdist_wheel for nltk ... done
  Stored in directory: /Users/falehalrashidi/Library/Caches/pip/wheels/18/9c/1f/276bc3f421614062468cb1c9d695e6086d0c73d67ea363c501
Successfully built nltk
Installing collected packages: nltk, textblob
  Found existing installation: nltk 3.2.4
    Uninstalling nltk-3.2.4:
      Successfully uninstalled nltk-3.2.4
Successfully installed nltk-3.2.5 textblob-0.15.1

In [8]:

    

! python -m textblob.download_corpora


    

    




[nltk_data] Downloading package brown to
[nltk_data]     /Users/falehalrashidi/nltk_data...
[nltk_data]   Package brown is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     /Users/falehalrashidi/nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package wordnet to
[nltk_data]     /Users/falehalrashidi/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /Users/falehalrashidi/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!
[nltk_data] Downloading package conll2000 to
[nltk_data]     /Users/falehalrashidi/nltk_data...
[nltk_data]   Package conll2000 is already up-to-date!
[nltk_data] Downloading package movie_reviews to
[nltk_data]     /Users/falehalrashidi/nltk_data...
[nltk_data]   Package movie_reviews is already up-to-date!
Finished.

In [9]:

    

from textblob import TextBlob


    

In [10]:

    

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.
'''


    

In [11]:

    

blob = TextBlob(text)


    

In [12]:

    

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


    

    
Out[12]:





[('The', 'DT'),
 ('titular', 'JJ'),
 ('threat', 'NN'),
 ('of', 'IN'),
 ('The', 'DT'),
 ('Blob', 'NNP'),
 ('has', 'VBZ'),
 ('always', 'RB'),
 ('struck', 'VBN'),
 ('me', 'PRP'),
 ('as', 'IN'),
 ('the', 'DT'),
 ('ultimate', 'JJ'),
 ('movie', 'NN'),
 ('monster', 'NN'),
 ('an', 'DT'),
 ('insatiably', 'RB'),
 ('hungry', 'JJ'),
 ('amoeba-like', 'JJ'),
 ('mass', 'NN'),
 ('able', 'JJ'),
 ('to', 'TO'),
 ('penetrate', 'VB'),
 ('virtually', 'RB'),
 ('any', 'DT'),
 ('safeguard', 'NN'),
 ('capable', 'JJ'),
 ('of', 'IN'),
 ('as', 'IN'),
 ('a', 'DT'),
 ('doomed', 'JJ'),
 ('doctor', 'NN'),
 ('chillingly', 'RB'),
 ('describes', 'VBZ'),
 ('it', 'PRP'),
 ('assimilating', 'VBG'),
 ('flesh', 'NN'),
 ('on', 'IN'),
 ('contact', 'NN'),
 ('Snide', 'JJ'),
 ('comparisons', 'NNS'),
 ('to', 'TO'),
 ('gelatin', 'VB'),
 ('be', 'VB'),
 ('damned', 'VBN'),
 ('it', 'PRP'),
 ("'s", 'VBZ'),
 ('a', 'DT'),
 ('concept', 'NN'),
 ('with', 'IN'),
 ('the', 'DT'),
 ('most', 'RBS'),
 ('devastating', 'JJ'),
 ('of', 'IN'),
 ('potential', 'JJ'),
 ('consequences', 'NNS'),
 ('not', 'RB'),
 ('unlike', 'IN'),
 ('the', 'DT'),
 ('grey', 'NN'),
 ('goo', 'NN'),
 ('scenario', 'NN'),
 ('proposed', 'VBN'),
 ('by', 'IN'),
 ('technological', 'JJ'),
 ('theorists', 'NNS'),
 ('fearful', 'NN'),
 ('of', 'IN'),
 ('artificial', 'JJ'),
 ('intelligence', 'NN'),
 ('run', 'NN'),
 ('rampant', 'NN')]

In [13]:

    

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


    

    
Out[13]:





WordList(['titular threat', 'blob', 'ultimate movie monster', 'amoeba-like mass', 'snide', 'potential consequences', 'grey goo scenario', 'technological theorists fearful', 'artificial intelligence run rampant'])

In [14]:

    

for sentence in blob.sentences:
    print(sentence.sentiment.polarity)


    

    




0.06000000000000001
-0.34166666666666673

In [ ]: