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| import pandas as pd | |
| from collections import Counter | |
| import re | |
| import plotly.express as px | |
| import streamlit as st | |
| # Sample DataFrame (replace with your actual data) | |
| data = { | |
| 'headline': [ | |
| "Europe is investing in renewable energy.", | |
| "China's economic growth is surpassing expectations.", | |
| "U.S. and Russia are in talks about trade.", | |
| "Arab countries are leading in oil production.", | |
| "Asia's tech industry is booming." | |
| ], | |
| 'body': [ | |
| "The renewable energy sector in Europe has seen increased investments.", | |
| "China continues to grow economically, beating many forecasts.", | |
| "The United States and Russia have started trade negotiations amid tensions.", | |
| "Oil production in Arab countries is at a new high.", | |
| "Asia is becoming a leader in the global tech industry." | |
| ] | |
| } | |
| df = pd.DataFrame(data) | |
| # List of stopwords (you can customize this or use libraries like NLTK) | |
| stop_words = set(['is', 'in', 'are', 'and', 'the', 'of', 'a', 'to']) | |
| # Function to clean, tokenize, and remove stopwords | |
| def tokenize(text): | |
| # Remove punctuation and convert text to lowercase | |
| text = re.sub(r'[^\w\s]', '', text.lower()) | |
| # Tokenize by splitting the string into words | |
| words = text.split() | |
| # Remove stopwords | |
| filtered_words = [word for word in words if word not in stop_words] | |
| return filtered_words | |
| # Tokenize and count word frequencies across the entire DataFrame | |
| all_words = [] | |
| df['headline'].apply(lambda x: all_words.extend(tokenize(x))) | |
| df['body'].apply(lambda x: all_words.extend(tokenize(x))) | |
| # Count word frequencies | |
| word_counts = Counter(all_words) | |
| # Get the top 10 most common words | |
| top_10_words = word_counts.most_common(10) | |
| # Convert the top 10 words to a DataFrame for plotting | |
| top_10_df = pd.DataFrame(top_10_words, columns=['word', 'frequency']) | |
| # Streamlit setup | |
| st.title('Interactive Word Frequency Analysis') | |
| # Plot the bar chart using Plotly | |
| fig = px.bar(top_10_df, x='word', y='frequency', title='Top 10 Most Common Words (Excluding Stopwords)', | |
| labels={'word': 'Word', 'frequency': 'Frequency'}, | |
| text='frequency') | |
| # Display the Plotly chart in Streamlit | |
| st.plotly_chart(fig) | |
| # Sidebar filter for word selection, simulating a "click" on the bar chart | |
| clicked_word = st.selectbox("Select a word to filter headlines:", options=top_10_df['word']) | |
| # Filter the DataFrame for headlines that contain the selected word | |
| filtered_df = df[df['headline'].str.contains(clicked_word, case=False)] | |
| # Display filtered headlines | |
| if not filtered_df.empty: | |
| st.subheader(f"Headlines containing the word '{clicked_word}'") | |
| st.table(filtered_df[['headline']]) | |
| else: | |
| st.write(f"No headlines found containing the word '{clicked_word}'") | |