import nltk 
from nltk.corpus import stopwords 
from nltk.tokenize import word_tokenize, sent_tokenize 
stop_words = set(stopwords.words('english')) 

# Dummy text 
txt = "Sukanya, Rajib and Naba are my good friends. " \
	"Sukanya is getting married next year. " \
	"Marriage is a big step in one’s life." \
	"It is both exciting and frightening. " \
	"But friendship is a sacred bond between people. " \
	"It is a special kind of love between us. " \
	"Many of you must have tried searching for a friend " \
	"but never found the right one. " \
	"Can you please buy me an Arizona Ice Tea? It's $0.99."

# sent_tokenize is one of instances of 
# PunktSentenceTokenizer from the nltk.tokenize.punkt module 

tokenized = sent_tokenize(txt) 
for i in tokenized: 
	
	# Word tokenizers is used to find the words 
	# and punctuation in a string 
	wordsList = nltk.word_tokenize(i) 

	# removing stop words from wordList 
	#wordsList = [w for w in wordsList if not w in stop_words] 

	# Using a Tagger. Which is part-of-speech 
	# tagger or POS-tagger. 
	tagged = nltk.pos_tag(wordsList) 

	print(tagged) 

