Upload preprocessor.py

preprocessor.py

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+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