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import re
import pandas as pd
from nltk.corpus import stopwords
import nltk
nltk.download('stopwords')
def preprocess(data):
pattern = '\d{1,2}/\d{1,2}/\d{2,4},\s\d{1,2}:\d{2}\s-\s'
messages = re.split(pattern, data)[1:]
dates = re.findall(pattern, data)
df = pd.DataFrame({'user_message': messages, 'message_date': dates})
# convert message_date type
df['message_date'] = pd.to_datetime(df['message_date'], format='%d/%m/%Y, %H:%M - ')
df.rename(columns={'message_date': 'date'}, inplace=True)
users = []
messages = []
for message in df['user_message']:
entry = re.split('([\w\W]+?):\s', message)
if entry[1:]: # user name
users.append(entry[1])
messages.append(" ".join(entry[2:]))
else:
users.append('group_notification')
messages.append(entry[0])
df['user'] = users
df['message'] = messages
df.drop(columns=['user_message'], inplace=True)
# Additional preprocessing
stop_words_en = set(stopwords.words('english'))
stop_words_fr = set(stopwords.words('french'))
combined_stop_words = stop_words_en.union(stop_words_fr)
def clean_message(message):
# Remove messages containing '<Media ...>'
if '<Médias omis>' in message:
return ''
# Remove words with less than 4 characters
words = [word for word in message.split() if len(word) >= 4]
# Remove stopwords
words = [word for word in words if word.lower() not in combined_stop_words]
return ' '.join(words)
df['message'] = df['message'].apply(clean_message)
df = df[df['message'] != ''] # Remove empty messages
df['only_date'] = df['date'].dt.date
df['year'] = df['date'].dt.year
df['month_num'] = df['date'].dt.month
df['month'] = df['date'].dt.month_name()
df['day'] = df['date'].dt.day
df['day_name'] = df['date'].dt.day_name()
df['hour'] = df['date'].dt.hour
df['minute'] = df['date'].dt.minute
period = []
for hour in df[['day_name', 'hour']]['hour']:
if hour == 23:
period.append(str(hour) + "-" + str('00'))
elif hour == 0:
period.append(str('00') + "-" + str(hour + 1))
else:
period.append(str(hour) + "-" + str(hour + 1))
df['period'] = period
return df
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