Datasets:

rr

/

dd

like 0

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