text
stringlengths 0
207
|
|---|
words = remove_punctuation(words)
|
#print(words)
|
def replace_numbers(words):
|
#'''Replace all integer occurrences in the list of tokenized words'''
|
p = inflect.engine()
|
new_words = []
|
for word in words:
|
if word.isdigit():
|
new_word = p.number_to_words(word)
|
new_words.append(new_word)
|
else:
|
new_words.append(word)
|
return(new_words)
|
words = replace_numbers(words)
|
#print(words)
|
def remove_stopwords(words):
|
#'''Remove stop words from the list of tokenized words'''
|
new_words = []
|
for word in words:
|
if word not in stopwords.words('english'):
|
new_words.append(word)
|
return new_words
|
words = remove_stopwords(words)
|
#print(words)
|
def stem_words(words):
|
#'''Finding stem words in the list of tokenized words'''
|
stemmer = LancasterStemmer()
|
stems = []
|
for word in words:
|
stem = stemmer.stem(word)
|
stems.append(stem)
|
return stems
|
words = stem_words(words)
|
#print(words)
|
def lemmatize_words(words):
|
#'''Lemmatize verbs in the list of tokenized words'''
|
lemmatizer = WordNetLemmatizer()
|
lemmas = []
|
for word in words:
|
lemma = lemmatizer.lemmatize(word, pos = 'v')
|
lemmas.append(lemma)
|
return lemmas
|
words = lemmatize_words(words)
|
#print(words)
|
print(words)
|
Text preprocessing(non user defined)
|
import nltk
|
import re
|
import string
|
import inflect
|
from nltk.corpus import stopwords
|
from nltk import word_tokenize
|
series = open("dataset path.txt".txt").read()
|
series
|
series_lower = series.lower()
|
# Removal of numbers
|
result1 = re.sub(r'\d+', '', series_lower)
|
#result1
|
# Removal of punctuations
|
result2 = result1.translate(str.maketrans('','',string.punctuation))
|
#result2
|
# Removing white spaces
|
result3 = result2.strip()
|
#result3
|
# Removal of stopwords
|
# Tokenize the text
|
result3_tokens = word_tokenize(result3)
|
#result3_tokens
|
# Removing stopwords
|
sw = set(stopwords.words('english'))
|
result4 = []
|
for w in result3_tokens:
|
if w not in sw:
|
result4.append(w)
|
#result4
|
text_tokenize = result4
|
#text_tokenize
|
output = nltk.pos_tag(text_tokenize)
|
#output
|
Sentiment Analysis
|
import pandas as pd
|
import re
|
import string
|
from nltk.tokenize import word_tokenize
|
from nltk.corpus import stopwords
|
from nltk.stem import PorterStemmer
|
from nltk.stem import WordNetLemmatizer
|
import nltk
|
from wordcloud import WordCloud
|
import matplotlib.pyplot as plt
|
file = open("dataset path.txt".txt", encoding = 'utf-8').read()
|
# These are not required. DO Only if asked.
|
# this code, clean data 2 and clean data 3
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.