Delete preprocessor.py

preprocessor.py

@@ -1,111 +0,0 @@
-import pandas as pd
-import re
-from textblob import TextBlob
-import numpy as np
-import nltk
-import nltk.data
-from nltk.sentiment.vader import SentimentIntensityAnalyzer
-from tqdm.notebook import tqdm
-sia=SentimentIntensityAnalyzer()
-nltk.download('vader_lexicon')
-
-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})
-    df['message_date'] = pd.to_datetime(df['message_date'], format='%m/%d/%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:]:
-            users.append(entry[1])
-            messages.append(entry[2])
-
-        else:
-            users.append('group_notification')
-            messages.append(entry[0])
-    df['users'] = users
-    df['message'] = messages
-    df.drop(columns=['user_message'], inplace=True)
-    df['year'] = df['date'].dt.year
-    df['day'] = df['date'].dt.day
-    df['hour'] = df['date'].dt.hour
-    df['minute'] = df['date'].dt.minute
-    df['Day_name'] = df['date'].dt.day_name()
-    df['Date']=df['date'].dt.date
-    df['Month'] = df['date'].dt.month
-    df['Month_name'] = df['date'].dt.month_name()
-
-    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
-
-    temp = df[df['users'] != 'group_notification']
-    temp = temp[temp['message'] != '<Media omitted>\n']
-    temp.replace("", np.nan, inplace=True)
-    temp = temp.dropna()
-
-    def cleanTxt(text):
-        text = re.sub(r'@[A-Za-z0-9]+', '', text)
-        text = re.sub(r'#', '', text)
-        text = text.replace('\n', "")
-        return text
-
-    temp['message'] = temp['message'].apply(cleanTxt)
-    temp['users'] = temp['users'].apply(cleanTxt)
-
-    res = {}
-    for i, row in tqdm(temp.iterrows(), total=len(temp)):
-        text = row['message']
-        myid = row['users']
-        res[myid] = sia.polarity_scores(text)
-
-    vaders = pd.DataFrame(res).T
-    vaders = vaders.reset_index().rename(columns={'index': 'users'})
-    vaders = vaders.merge(temp, how="right")
-    vaders_new = vaders.pop('message')
-    vaders_new = pd.DataFrame(vaders_new)
-    vaders.insert(1, "message", vaders_new['message'])
-
-    def getSubjectivity(text):
-        return TextBlob(text).sentiment.subjectivity
-
-    def getPolarity(text):
-        return TextBlob(text).sentiment.polarity
-
-    vaders['Subjectivity'] = vaders['message'].apply(getSubjectivity)
-    vaders['Polarity'] = vaders['message'].apply(getPolarity)
-
-    def getAnalysis(score):
-        if score < 0:
-            return 'Negative'
-        if score == 0:
-            return 'Neutral'
-        else:
-            return 'Positive'
-
-    vaders['Analysis'] = vaders['Polarity'].apply(getAnalysis)
-
-    def getAnalysis(score):
-        if score <= 0:
-            return 'Negative'
-        if score < 0.2960:
-            return 'Neutral'
-        else:
-            return 'Positive'
-
-    vaders['vader_Analysis'] = vaders['compound'].apply(getAnalysis)
-
-    return vaders