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import streamlit as st
import datetime
import pandas as pd
from gnews import GNews
from transformers import pipeline
import plotly.graph_objects as go
# Load the sentiment analysis model
pipe = pipeline("text-classification", model="pramudyalyza/bert-indonesian-finetuned-news")
# Function to process the keyword and get sentiment analysis
def process_keyword(keyword):
one_week_ago = datetime.datetime.now() - datetime.timedelta(days=7)
news = GNews(language='id', country='ID', max_results=100)
search_results = news.get_news(keyword)
filtered_headlines = []
for article in search_results:
published_date = datetime.datetime.strptime(article['published date'], '%a, %d %b %Y %H:%M:%S %Z')
if published_date > one_week_ago:
filtered_headlines.append(article['title'])
df = pd.DataFrame(filtered_headlines, columns=['title'])
df_clean = df.drop_duplicates()
df_clean['sentiment'] = df_clean['title'].apply(lambda x: pipe(x)[0]['label'])
positive_count = (df_clean['sentiment'] == 'Positive').sum()
negative_count = (df_clean['sentiment'] == 'Negative').sum()
total_count = len(df_clean)
return positive_count, negative_count, total_count, df_clean
# Streamlit app layout
st.title("News Sentiment Analysis Dashboard")
keyword_input = st.text_input("Enter a keyword to search for news", placeholder="Type a keyword...")
if st.button("Analyze"):
if keyword_input:
with st.spinner('Scraping and analyzing the data...'):
positive_count, negative_count, total_count, df_clean = process_keyword(keyword_input)
# Create plots
fig_positive = go.Figure(go.Indicator(
mode="gauge+number",
value=positive_count,
title={'text': "Positive Sentiment"},
gauge={'axis': {'range': [0, total_count]},
'bar': {'color': "green"}}
))
fig_negative = go.Figure(go.Indicator(
mode="gauge+number",
value=negative_count,
title={'text': "Negative Sentiment"},
gauge={'axis': {'range': [0, total_count]},
'bar': {'color': "red"}}
))
fig_donut = go.Figure(go.Pie(
labels=['Positive', 'Negative'],
values=[positive_count, negative_count],
hole=0.5,
marker=dict(colors=['green', 'red'])
))
fig_donut.update_layout(title_text='Sentiment Distribution')
# Create a horizontal layout using st.columns
col1, col2, col3 = st.columns(3)
# Display results in each column
col1.plotly_chart(fig_positive, use_container_width=True)
col2.plotly_chart(fig_negative, use_container_width=True)
col3.plotly_chart(fig_donut, use_container_width=True)
st.write(f"News articles found: {total_count}")
# Show DataFrame
st.dataframe(df_clean, use_container_width=True)
# Download CSV
csv = df_clean.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download CSV",
data=csv,
file_name='news_sentiment_analysis.csv',
mime='text/csv',
)
else:
st.error("Please enter a keyword.") |