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import pandas as pd
import numpy as np
import nltk
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
from sklearn.feature_extraction.text import CountVectorizer
import csv
nltk.download('stopwords')
nltk.download('punkt')
stopwords_es = stopwords.words('spanish')
spanish_stemmer = SnowballStemmer('spanish')
def remove_html_markup(s):
tag = False
quote = False
out = ""
for c in s:
if c == '<' and not quote:
tag = True
elif c == '>' and not quote:
tag = False
elif (c == '"' or c == "'") and tag:
quote = not quote
elif not tag:
out = out + c
return out
def remove_URL(s):
"""Remove URLs from a sample string"""
return re.sub(r"http\S+", "", s)
def eliminar_puntuacion(articulo):
deletetion_symbols = ['!','(',')',"'",'-','[',']','{','}',';',':','"','“','’','”',"'",'`','‘','``','\\' ,'/','|',',','|','<','>','.','..','...','?','@',"#",'$','^','&','*','_','~','+','%','=','¿','¡',"''"]
new_articulo = ""
for x in articulo:
if x not in deletetion_symbols:
new_articulo += x
return new_articulo
def eliminar_stopwords(articulo):
articulo_splitted=articulo.split()
new_articulo = ""
for x in articulo_splitted:
if x not in stopwords_es:
new_articulo += " " + x
return new_articulo
def obtener_raices(articulo):
articulo_splitted=articulo.split()
new_articulo = ""
for x in articulo_splitted:
x_new = spanish_stemmer.stem(x)
new_articulo += " " + x_new
return new_articulo
def limpieza_articulos(df):
df_titulos=pd.DataFrame(df['titulo'], columns=['titulo'])
# Colocando texto en minusculas
df_titulos['titulo']= df_titulos['titulo'].apply(lambda x: x.lower())
# Eliminando signos de puntuacion
df_titulos['titulo']= df_titulos['titulo'].apply(lambda x: eliminar_puntuacion(x))
# Eliminando palabras vacias (stopwords) utilizando el corpus para estos fines que tiene nltk
df_titulos['titulo']= df_titulos['titulo'].apply(lambda x: eliminar_stopwords(x))
all_text = ' '. join(df_titulos['titulo'])
vocab= np.unique(word_tokenize(all_text))
return vocab
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