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NLP.py
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import numpy as np
import pandas as pd
import re
import nltk
from nltk import word_tokenize, sent_tokenize
from nltk.tokenize import sent_tokenize, word_tokenize
from cleantext import clean
from nltk.stem import WordNetLemmatizer
nltk.download('tagsets')
nltk.help.upenn_tagset('NNP')
nltk.help.upenn_tagset('NN')
nltk.download('maxent_ne_chunker')
nltk.download('words')
nltk.download('averaged_perceptron_tagger')
try:
with open("test1_2.txt", "r", encoding="utf-8") as file:
text_data = file.read()
except:
print("There is not such a file or path is incorrect")
text_data_clean = clean(text_data,
fix_unicode=True,
to_ascii=False,
lower=False,
normalize_whitespace=True,
no_line_breaks=True,
strip_lines=False,
keep_two_line_breaks=False,
no_urls=True,
no_emails=True,
no_phone_numbers=True,
no_numbers=True,
no_digits=True,
no_currency_symbols=True,
no_punct=False,
no_emoji=True,
replace_with_url='',
replace_with_email='',
replace_with_phone_number='',
replace_with_number='',
replace_with_digit='',
replace_with_currency_symbol='',
replace_with_punct=''
)
text_data_clean_apos = re.sub(r"\'", "", string=text_data_clean)
text_data_clean_brackets = re.sub('[\(\[\{].*?[\)\]\}]', '', text_data_clean_apos)
custom_char = ["-", "#", ":"]
for i in custom_char:
text_data_clean_brackets = text_data_clean_brackets.replace(i, '') # must be equal each other
word_tokens = nltk.word_tokenize(text_data_clean_brackets)
pos_tags = nltk.pos_tag(word_tokens)
chunks = nltk.ne_chunk(pos_tags, binary=True)
entities = []
labels = []
for chunk in chunks:
if hasattr(chunk, 'label'):
# print(chunk)
entities.append(' '.join(c[0] for c in chunk))
labels.append(chunk.label())
entities_labels = list(set(zip(entities, labels)))
entities_df = pd.DataFrame(entities_labels)
entities_df.columns = ["Entities", "Labels"]
entities_list = []
for i in entities_labels:
entities_list.append(i[0])
for ent in entities_list:
text_data_clean_brackets = text_data_clean_brackets.replace(ent, '')
text_data_clean_ne = text_data_clean_brackets
var1 = re.findall(r'\w+.', text_data_clean_ne)
var2 = " ".join(var1)
text_data_clean_punc_alone = var2
text_data_clean2 = clean(text_data_clean_punc_alone,
fix_unicode=True,
to_ascii=False,
lower=False,
normalize_whitespace=True,
no_line_breaks=True,
strip_lines=False,
keep_two_line_breaks=False,
no_urls=True,
no_emails=True,
no_phone_numbers=True,
no_numbers=True,
no_digits=True,
no_currency_symbols=True,
no_punct=False,
no_emoji=True,
replace_with_url='',
replace_with_email='',
replace_with_phone_number='',
replace_with_number='',
replace_with_digit='',
replace_with_currency_symbol='',
replace_with_punct=''
) # White space
word_tokens_2 = word_tokenize(text_data_clean2.lower())
tokens_without_punc = [w for w in word_tokens_2 if w.isalpha()]
tokens_without_punc_lemma = []
for word in tokens_without_punc:
tokens_without_punc_lemma.append(WordNetLemmatizer().lemmatize(word))
tokens_without_punc = pd.Series(tokens_without_punc_lemma)
tokens_without_punc = tokens_without_punc.value_counts().sort_values(ascending=False)
freq_word_df = pd.DataFrame(tokens_without_punc)
freq_word_df = freq_word_df.reset_index()
freq_word_df = freq_word_df.rename(columns={"index": "word", 0: "frequency"})
freq_word_df["ratio"] = round((freq_word_df.frequency / (sum(freq_word_df.frequency)) * 100), 7)
freq_word_df["cumul_ratio"] = np.cumsum(freq_word_df["ratio"])
freq_word_df_5 = freq_word_df[freq_word_df.frequency >= 5]
freq_word_df_5.to_excel("Word_Tokenize.xlsx", sheet_name='word_tokenize_5', index=False)