TextBlob | speech tagging , noun phrase extraction | Natural Language Processing library

$ python examples/textblob_example.py
$ pip install httpie
$ http POST :5000/api/v1/analyze text="Simple is better"
HTTP/1.0 200 OK
Content-Length: 189
Content-Type: application/json
Date: Wed, 13 Nov 2013 08:58:40 GMT
Server: WSGIServer/0.1 Python/2  

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
de  

from bottle import route, request, run
from textblob import TextBlob
from marshmallow import Schema, fields


class BlobSchema(Schema):
    polarity = fields.Float()
    subjectivity = fields.Float()
    chunks = fields.List(fields.String, attribute=  

from textblob import TextBlob

sent = 'a very simple and good sample'
pos_word_list = []
neg_word_list = []
neu_word_list = []

for word in sent.split():
    testimonial = TextBlob(word)
    if testimonial.sentiment.polarity >= 0.5:
   

df['sentiment'] = np.select(
    [
        dataset['polarity'] > 0,
        dataset['polarity'] == 0
    ],
    [
        "Positive",
        "Neutral"
    ],
    default="Negative"
)

df['sentiment'] = np.select

import re
from textblob import TextBlob
from textblob import Word
rx = re.compile(r'([^\W\d_])\1{2,}')
print( re.sub(r'[^\W\d_]+', lambda x: Word(rx.sub(r'\1\1', x.group())).correct() if rx.search(x.group()) else x.group(), tweet) )
# =>

import pandas as pd
import nltk
from textblob import TextBlob
import functools
import operator

df = pd.DataFrame({'text': ["spellling", "was", "working cooking listening","studying"]})

#tokenization
w_tokenizer = nltk.tokenize.Whitespace