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cleandata1 = file.lower()
#cleandata1
cleandata2 = re.sub(r'[^\w\s]','', cleandata1)
#cleandata2
cleandata3 = re.sub(r'\d+', ' ', cleandata2)
#cleandata3
stop_words = set(stopwords.words('english'))
#stop_words
#let us remove them using function removeWords()
tokens = word_tokenize(cleandata3)
cleandata4 = [i for i in tokens if not i in stop_words]
cleandata4
cleandata4 = " ".join(str(x) for x in cleandata4)
#cleandata4
cleandata5 = ' '.join(i for i in cleandata4.split() if not (i.isalpha() and len(i)==1))
#cleandata5
cleandata6 = cleandata5.strip()
#cleandata6
## Frequency of words
words_dict = {}
for word in cleandata6.split():
words_dict[word] = words_dict.get(word, 0)+1
for key in sorted(words_dict):
print("{}:{}".format(key,words_dict[key]))
wordcloud = WordCloud(width=480, height=480, margin=0).generate(cleandata6)
# Display the generated image:
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.margins(x=0, y=0)
plt.show()
#with max words
wordcloud = WordCloud(width=480, height=480, max_words=5).generate(cleandata6)
plt.figure()
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.margins(x=0, y=0)
plt.show()
from textblob import TextBlob
from textblob.sentiments import NaiveBayesAnalyzer
Bag of Words
from sklearn.feature_extraction.text import CountVectorizer
sentences = ["Hello how are you",
"Hi students are you all good",
"Okay lets study bag of words"]
sentences
cv = CountVectorizer()
bow = cv.fit_transform(sentences).toarray()
cv.vocabulary_
cv.get_feature_names()
bow
NLTK Basics
import nltk
from nltk.book import *
#similar
text6.similar('King')
text6.concordance('King')
sents()
len(text1)
#lines tells how many lines you want. You can run the code without the lines also
text3.concordance('lived', lines = 38)
text3.common_contexts(['earth', 'heaven'])
text1.common_contexts(['captain', 'whale'])
#text3.collocations()
text3.collocation_list()
#Put number inside bracket to get only how many is required
text6.collocation_list(5)
text6.generate(5)
len(text3)
from nltk import lm
help(lm)
text = "Hello students, we are studying Parts of Speech Tagging. Lets understand the process of\
shallow parsing or Chunking. Here were are drawing the tree corresponding to the words \
and the POS tags based on a set grammer regex patter."
words = nltk.word_tokenize(text)
#words
tags = nltk.pos_tag(words)
#tags
# idk what this is
grammar = (''' NP: {<DT><JJ><NN>} ''')
grammar
freq = FreqDist(text3)
freq
freq.most_common(50)
freq['father']
freq.plot(20, cumulative = True)
freq.plot(20)
freq.tabulate()
freq.max()
[i for i in sent3 if len(i) > 8]
[i for i in sent3 if len(i) != 3]
[i for i in sent3 if len(i) <= 3]
l = []
for i in sent3:
if((len(i)) <= 3):
l.append(i)
print(l)