Create text_analyzer.py

text_analyzer.py

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+import streamlit as st
+import pandas as pd
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.sentiment import SentimentIntensityAnalyzer
+from collections import Counter
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('vader_lexicon')
+
+class TextAnalyzer:
+    def analyze_text(self, df):
+        text_columns = df.select_dtypes(include=['object']).columns
+        
+        if len(text_columns) > 0:
+            text_column = st.selectbox("Select text column for analysis", text_columns)
+            analysis_type = st.selectbox("Select analysis type", ["Word Frequency", "Sentiment Analysis", "Word Cloud"])
+            
+            if analysis_type == "Word Frequency":
+                self.perform_word_frequency(df[text_column])
+            elif analysis_type == "Sentiment Analysis":
+                self.perform_sentiment_analysis(df[text_column])
+            elif analysis_type == "Word Cloud":
+                self.generate_word_cloud(df[text_column])
+        else:
+            st.write("No text columns found in the dataset.")
+
+    def perform_word_frequency(self, text_series):
+        stop_words = set(stopwords.words('english'))
+        word_freq = Counter()
+        
+        for text in text_series:
+            tokens = word_tokenize(text.lower())
+            words = [word for word in tokens if word.isalnum() and word not in stop_words]
+            word_freq.update(words)
+        
+        st.subheader("Word Frequency Analysis")
+        n_words = st.slider("Select number of top words to display", min_value=5, max_value=50, value=20)
+        
+        top_words = word_freq.most_common(n_words)
+        fig = px.bar(x=[word for word, _ in top_words], y=[freq for _, freq in top_words], title="Top Words")
+        st.plotly_chart(fig)
+
+    def perform_sentiment_analysis(self, text_series):
+        sia = SentimentIntensityAnalyzer()
+        sentiments = text_series.apply(lambda x: sia.polarity_scores(x))
+        
+        st.subheader("Sentiment Analysis")
+        sentiment_df = pd.DataFrame(sentiments.tolist())
+        
+        fig = px.histogram(sentiment_df, x='compound', title="Sentiment Distribution")
+        st.plotly_chart(fig)
+        
+        st.write("Average Sentiment Scores:")
+        st.write(sentiment_df.mean())
+
+    def generate_word_cloud(self, text_series):
+        stop_words = set(stopwords.words('english'))
+        text = ' '.join(text_series)
+        
+        wordcloud = WordCloud(width=800, height=400, background_color='white', stopwords=stop_words).generate(text)
+        
+        st.subheader("Word Cloud")
+        fig, ax = plt.subplots()
+        ax.imshow(wordcloud, interpolation='bilinear')
+        ax.axis('off')
+        st.pyplot(fig)