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import matplotlib.pyplot as plt
from urlextract import URLExtract
from collections import Counter
from wordcloud import WordCloud, STOPWORDS ,ImageColorGenerator
import pandas as pd
import matplotlib.pylab as plt
import PIL.Image
import numpy as np
import emoji
extract=URLExtract()
def fetch_stats(selected_user,df):
if selected_user!= "Group analysis":
df=df[df['users']==selected_user]
num_messages = df.shape[0]
words = []
for message in df['message']:
words.extend(message.split())
links=[]
for message in df['message']:
links.extend(extract.find_urls(message))
return num_messages, len(words),len(links)
def most_busy_users(df):
x = df['users'].value_counts().head()
df=round((df['users'].value_counts() / df.shape[0]) * 100, 2).reset_index().rename(
columns={'index': 'name', 'user': 'percent'})
return x,df
def most_common_words(selected_user,df):
f = open('stop_hinglish.txt', 'r')
stop_words = f.read()
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
temp = df[df['users'] != 'group_notification']
temp = temp[temp['message'] != '<Media omitted>\n']
words = []
for message in temp['message']:
for word in message.lower().split():
if word not in stop_words:
words.append(word)
most_common_df=pd.DataFrame(Counter(words).most_common(30))
return most_common_df
def word_cloud(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
stopwords = set('STOPWORDS')
# wordcloud
wordcloud = WordCloud(stopwords=stopwords, background_color="Black").generate(''.join(df['message']))
plt.figure(figsize=(10, 8), facecolor='k')
plt.imshow(wordcloud, interpolation='bilinear')
plt.show()
return wordcloud
def emoji_helper(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
emojis = []
for message in df['message']:
emojis.extend([c for c in message if c in emoji.EMOJI_DATA.keys()])
emoji_df=pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
return emoji_df
def monthly_timeline(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
timeline = df.groupby(['year', 'Month_name', 'Month']).count()['message'].reset_index()
time = []
for i in range(timeline.shape[0]):
time.append(timeline['Month_name'][i] + "-" + str(timeline['year'][i]))
timeline['time'] = time
return timeline
def Daily_timeline(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
daily_timeline = df.groupby('Date').count()['message'].reset_index()
return daily_timeline
def week_activity_map(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
return df['Day_name'].value_counts()
def month_activity_map(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
return df['Month_name'].value_counts()
def activity_heatmap(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
Activity_heatmap= df.pivot_table(index='Day_name', columns='period', values='message', aggfunc='count').fillna(0)
return Activity_heatmap
def pos_words(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
pos_word = df[df['vader_Analysis'] == 'Positive']
pos_word = pos_word.pop('message')
return pos_word
def neg_words(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
neg_word = df[df['Analysis'] == 'Negative']
neg_word = neg_word.pop('message')
return neg_word
def neu_words(selected_user,df):
if selected_user != "Group analysis":
df = df[df['users'] == selected_user]
neu_word = df[df['vader_Analysis'] == 'Neutral']
neu_word = neu_word.pop('message')
return neu_word |