Python_sentimental_analysis_and_WordCloud

Sentimental analysis metrics are computed for a big text, also a WordCloud.

-- coding: utf-8 --

""" Created on Fri Mar 5 22:47:02 2021

@author: Aaron """

import pandas as pd from textblob import TextBlob import seaborn as sns import os from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt

#VAMOS A REALIZAR UN ANALISIS DE SENTIMIENTOS DE UN TEXTO QUE CONTIENE REGISROS DE TEXTO os.getcwd() os.chdir('C:\Users\Aaron\Desktop\Boehringer\Codes\Python') os.getcwd() df=pd.read_excel('F1_2.xlsx')#si funciona df.to_csv ("Test.csv",index = None,header=True) df = pd.DataFrame(pd.read_csv("Test.csv")) print(df.dtypes) print(df.head(4)) df['Polaridad']=df['Comentarios'].apply(lambda x: TextBlob(x).sentiment.polarity) print(df['Polaridad'].head(4)) df['Sub-Obj']=df['Comentarios'].apply(lambda x: TextBlob(x).sentiment.subjectivity) print(df['Sub-Obj'].head(4)) df.head(5) df.describe() df.to_csv ("Test.csv",index = None,header=True)#LISTO; dataframe exportado sns.distplot(df['Polaridad']) sns.distplot(df['Sub-Obj'])

#AHORA VAMOS A CREAR LA NUBE DE PALBARAS:
df = pd.read_csv("1F_3.csv", encoding ="latin-1") df=pd.DataFrame(df) len(df) comment_words = '' stopwords = set(STOPWORDS)

print(stopwords.class) print(stopwords)

iterate through the csv file

for val in range(19638):

# typecaste each val to string 
val = str(df.values) 

# split the value 
tokens = val.split() 
  
comment_words += " ".join(tokens)+" "

wordcloud = WordCloud(width = 800, height = 800, background_color ='white', stopwords = stopwords, min_font_size = 10).generate(comment_words)

plot the WordCloud image

plt.figure(figsize = (8, 8), facecolor = None) plt.imshow(wordcloud) plt.axis("off") plt.tight_layout(pad = 0)

plt.show()