Question

# M-13 Assignment

# Adapt This Code (Originally From Analytics Vidhya) To Do The Following:

# 1. Accept a User Input for the Text to Remove Stopwords From

# 2. Print a Count of How Many Stopwords Were Removed

# 3. Display Both the Original and Filtered Sentences

# 4. Clean Up and Remove Unnecessary Code

# importing libraries

import nltk

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize

set(stopwords.words('english'))

# sample sentence

text = """He determined to drop his litigation with the monastry, and relinguish his claims to the wood-cuting and

fishery rihgts at once. He was the more ready to do this becuase the rights had become much less valuable, and he had

indeed the vaguest idea where the wood and river in question were."""

# set of stop words

stop_words = set(stopwords.words('english'))

# tokens of words

word_tokens = word_tokenize(text)

filtered_sentence = []

for w in word_tokens:

if w not in stop_words:

filtered_sentence.append(w)

print("\n\nOriginal Sentence \n\n")

print(" ".join(word_tokens))

print("\n\nFiltered Sentence \n\n")

print(" ".join(filtered_sentence))

Answer 1

Code has been explained in the comments itself:

Please refer to the screenshots for the indentation:

Please comment before downvoting, will resolve asap

Code :

# importing libraries
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

# sample sentence
text = """He determined to drop his litigation with the monastry, and relinguish his claims to the wood-cuting and
fishery rihgts at once. He was the more ready to do this becuase the rights had become much less valuable, and he had
indeed the vaguest idea where the wood and river in question were."""

# set of stop words
stop_words = set(stopwords.words('english'))

# tokens of words
word_tokens = word_tokenize(text)

# total number of words in the original sentence
total_words_original = len(word_tokens)

filtered_sentence = []

for w in word_tokens:
   if w not in stop_words:
       filtered_sentence.append(w)

# total number of words in the filtered sentence
total_words_filtered = len(filtered_sentence)

total_stop_words_removed = total_words_original - total_words_filtered
print("\n\nOriginal Sentence \n\n")
print(" ".join(word_tokens))
print("\n\nFiltered Sentence \n\n")
print(" ".join(filtered_sentence))

print("\n\nNumber of stop words removed : " + str(total_stop_words_removed) + "\n\n")

Screenshots:

Code:

Output: