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Review_Analyzer.py

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+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import nltk  # Natural Language Processing Toolkit
+from nltk.corpus import stopwords
+import re  # Natural Language Processing Toolkit
+from textblob import TextBlob # Python library for Sentiment analysis 
+
+# Stopwords for preprocessing
+nltk.download('stopwords')
+nltk.download('punkt')
+all_stopwords = stopwords.words('english')
+all_stopwords.remove('not')
+
+def text_preprocessor(text):
+
+    # To pre process text reviews
+    text = re.sub('[^a-zA-Z]', ' ', text)
+    text = text.lower()
+    a = [w for w in nltk.word_tokenize(text) if w not in all_stopwords]
+    return ' '.join(a)
+
+def get_sentiment(text):
+
+    # To analyze single review
+    text = text_preprocessor(text)
+    blob = TextBlob(text)
+    
+    polarity = round(blob.sentiment.polarity, 2)
+    subjectivity = round(blob.sentiment.subjectivity, 2)
+    
+    if polarity >= 0.3:
+        sentiment = 'Positive'
+    elif polarity <= 0.:
+        sentiment = 'Negative'
+    else:
+        sentiment = 'Neutral'
+    
+    return polarity, subjectivity, sentiment
+
+
+
+def analyze_data(file_path, file_format='csv'):
+
+    # Read data based on the specified file format
+    if file_format == 'csv':
+        data = pd.read_csv(file_path)
+    elif file_format == 'excel':
+        data = pd.read_excel(file_path)
+    elif file_format == 'parquet':
+        data = pd.read_parquet(file_path)
+
+    # Sentiment analysis
+    sentiment = []
+    subjectivity = []
+    polarity = []
+
+    preprocessed_data = data
+    preprocessed_data['Review'].apply(text_preprocessor)
+
+    for text in preprocessed_data['Review']:
+        pol, subj, sent = get_sentiment(text)
+        polarity.append(pol)
+        subjectivity.append(subj)
+        sentiment.append(sent)
+
+    data['Polarity'] = polarity
+    data['Sentiment'] = sentiment
+    data['Subjectivity'] = subjectivity
+
+    return data

main.py

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+import pandas as pd
+import streamlit as st
+from Review_Analyzer import *
+
+st.header('Review Analysis')
+with st.expander('Analyze Text'):
+    text = st.text_input('Text here: ')
+    if text:
+        text = get_sentiment(text)
+        st.write('Polarity: ', text[0])
+        st.write('Subjectivity: ', text[1])
+        st.write('Sentiment: ', text[2])
+
+with st.expander('Analyze CSV'):
+    upl = st.file_uploader('Upload file')
+    if upl:
+        file_format = st.selectbox('Select file format', ['csv', 'excel', 'parquet'])
+        df = analyze_data(upl, file_format=file_format)
+        st.write(df.head(10))
+
+        @st.cache_data
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv().encode('utf-8')
+        
+        csv = convert_df(df)
+
+        st.download_button(
+            label="Download data as CSV",
+            data=csv,
+            file_name='sentiment.csv',
+            mime='text/csv',
+        )