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from urlextract import URLExtract
from wordcloud import WordCloud
import pandas as pd
from collections import Counter
import emoji
import re
import numpy as np
import torch
extract = URLExtract()
def fetch_stats(selected_user,df):
if(selected_user!='Overall'):
df=df[df['user']==selected_user]
num_messages = df.shape[0]
words = []
for message in df['message']:
words.extend(message.split())
num_media_messages = df[df['message']=='<Media omitted>\n'].shape[0]
links=[]
for message in df['message']:
links.extend(extract.find_urls(message))
return num_messages, len(words), num_media_messages ,len(links)
def most_busy_users(df):
x = df['user'].value_counts()
x = x.head(min(10, len(x)))
new_df = round((df['user'].value_counts()/df.shape[0])*100,2).reset_index().rename(columns={'user':'name','count':'percent'})
return x,new_df
def create_wordcloud(selected_user,df):
f = open('stop_hinglish.txt', 'r')
stop_words = f.read()
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
temp = df[df['user'] != 'group_notification']
temp = temp[temp['message'] != '<Media omitted>\n']
def remove_stop_words(message):
y = []
for word in message.lower().split():
if word not in stop_words:
y.append(word)
return " ".join(y)
wc = WordCloud(width=500,height=500,min_font_size=10,background_color='white')
temp['message'] = temp['message'].apply(remove_stop_words)
df_wc = wc.generate(temp['message'].str.cat(sep=" "))
return df_wc
def most_common_words(selected_user,df):
f = open('stop_hinglish.txt','r')
stop_words = f.read()
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
temp = df[df['user'] != '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):
for c in word:
if c not in emoji.UNICODE_EMOJI_ENGLISH:
words.append(word)
break
most_common_df = pd.DataFrame(Counter(words).most_common(20))
return most_common_df
def emoji_helper(selected_user,df):
if (selected_user != 'Overall'):
df = df[df['user'] == selected_user]
emojis=[]
for message in df['message']:
emojis.extend([c for c in message if c in emoji.UNICODE_EMOJI_ENGLISH])
emoji_df = pd.DataFrame(Counter(emojis).most_common(len(Counter(emojis))))
return emoji_df
def monthly_timeline(selected_user,df):
if (selected_user != 'Overall'):
df = df[df['user'] == selected_user]
timeline = df.groupby(['year','month_num','month']).count()['message'].reset_index()
time=[]
for i in range(timeline.shape[0]):
time.append(timeline['month'][i]+"-"+str(timeline['year'][i]))
timeline['time'] =time
return timeline
def daily_timeline(selected_user,df):
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
daily_timeline = df.groupby('only_date').count()['message'].reset_index()
return daily_timeline
def week_activity_map(selected_user,df):
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
return df['day_name'].value_counts()
def month_activity_map(selected_user,df):
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
return df['month'].value_counts()
def activity_heatmap(selected_user,df):
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
user_heatmap = df.pivot_table(index='day_name', columns='period', values='message', aggfunc='count').fillna(0)
return user_heatmap
def birth_dates(df):
birthdates = []
names = []
for i in range(df.shape[0]):
msg = df['message'][i].lower()
if (re.search('happy birthday', msg)):
if (re.findall('@[A-Za-z0-9]+', df['message'][i])):
users = re.findall('@[A-Za-z0-9]+', df['message'][i])
for user in users:
if user[1:] not in names:
names.append(user[1:])
birthdates.append(str(df['month'][i]) + " " + str(df['day'][i]))
return pd.DataFrame({'contacts':names,'birthdates':birthdates})
def sentiment_analysis(selected_user,df):
if selected_user != 'Overall':
df = df[df['user'] == selected_user]
# sample code
from transformers import BertTokenizer, BertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained("ganeshkharad/gk-hinglish-sentiment")
model = BertForSequenceClassification.from_pretrained("ganeshkharad/gk-hinglish-sentiment")
if df.shape[0]>600:
df=df.sample(n=600)
ans = []
for i in range(df.shape[0]):
encoded_input = tokenizer(df['message'].iloc[i], return_tensors='pt')
output = model(**encoded_input)
output = np.argmax(output.logits.detach().numpy())
if (output == 0):
ans.append('Negative-messages')
elif (output == 1):
ans.append('Neutral-messages')
else:
ans.append('Positive-messages')
# output contains 3 lables LABEL_0 = Negative ,LABEL_1 = Nuetral ,LABEL_2 = Positive
return pd.Series(Counter(ans)),df.shape[0]
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