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import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# Set custom theme and configuration
st.set_page_config(page_title="SentimentSense", page_icon="🧠", layout="wide")
# Add custom styling
st.markdown(
"""
<style>
.main {
background-color: #f4f4f9;
}
.stButton>button {
background-color: #4CAF50;
color: white;
border: None;
border-radius: 12px;
height: 40px;
width: 100%;
margin: 0.5rem 0;
}
.stDownloadButton>button {
background-color: #1E88E5;
color: white;
border: None;
border-radius: 12px;
height: 40px;
width: 100%;
margin: 0.5rem 0;
}
</style>
""", unsafe_allow_html=True
)
# Title and description with an emoji
st.title("🧠 SentimentSense: Your Text Analysis Companion")
st.markdown(
"""
**SentimentSense** provides in-depth sentiment analysis to understand the emotions conveyed in your text.
Simply input your text below and let the magic happen!
"""
)
# Initialize or load session state to keep track of the results
if 'results' not in st.session_state:
st.session_state['results'] = []
# Sidebar for text input and actions
st.sidebar.header("📝 Input Text for Sentiment Analysis")
text_input = st.sidebar.text_area("Enter text for sentiment analysis:", height=150)
clear_button = st.sidebar.button("Clear Input")
reset_button = st.sidebar.button("Reset Analysis History")
if clear_button:
text_input = ""
if reset_button:
st.session_state['results'] = []
# Function to analyze sentiment
def analyze_sentiment(text):
analyzer = SentimentIntensityAnalyzer()
sentiment = analyzer.polarity_scores(text)
return sentiment
# Function to get primary sentiment with an emoji
def get_primary_sentiment(sentiment):
if sentiment['compound'] >= 0.05:
return "Positive 😊"
elif sentiment['compound'] <= -0.05:
return "Negative 😠"
else:
return "Neutral 😐"
# Analyze button
if st.sidebar.button("Analyze Sentiment"):
if text_input:
sentiment = analyze_sentiment(text_input)
primary_sentiment = get_primary_sentiment(sentiment)
# Display results in the main area
st.subheader("Analysis Results")
col1, col2 = st.columns(2)
with col1:
st.metric(label="Primary Sentiment", value=primary_sentiment)
st.metric(label="Compound Score", value=round(sentiment['compound'], 2))
with col2:
st.metric(label="Positive Score", value=round(sentiment['pos'], 2))
st.metric(label="Neutral Score", value=round(sentiment['neu'], 2))
st.metric(label="Negative Score", value=round(sentiment['neg'], 2))
# Interactive Visualization - Pie Chart for Sentiment Breakdown
st.subheader("Sentiment Score Distribution")
pie_chart = px.pie(
names=['Positive', 'Neutral', 'Negative'],
values=[sentiment['pos'], sentiment['neu'], sentiment['neg']],
color=['Positive', 'Neutral', 'Negative'],
color_discrete_map={'Positive': '#00C853', 'Neutral': '#039BE5', 'Negative': '#D32F2F'},
title='Sentiment Breakdown'
)
st.plotly_chart(pie_chart, use_container_width=True)
# Interactive Visualization - Gauge Chart for Compound Score
gauge_chart = go.Figure(go.Indicator(
mode="gauge+number",
value=sentiment['compound'],
title={'text': "Compound Sentiment Score"},
gauge={'axis': {'range': [-1, 1]},
'bar': {'color': "#1E88E5"},
'steps': [
{'range': [-1, -0.05], 'color': '#D32F2F'},
{'range': [-0.05, 0.05], 'color': '#039BE5'},
{'range': [0.05, 1], 'color': '#00C853'}],
}))
st.plotly_chart(gauge_chart, use_container_width=True)
# Store results in session state
st.session_state['results'].append({
"Text": text_input,
"Primary Sentiment": primary_sentiment,
"Positive": sentiment['pos'],
"Neutral": sentiment['neu'],
"Negative": sentiment['neg'],
"Compound": sentiment['compound']
})
else:
st.warning("Please enter text for analysis.")
# Show past analysis results with a unique style
if st.session_state['results']:
st.subheader("📊 Sentiment Analysis History")
result_df = pd.DataFrame(st.session_state['results'])
# Interactive Timeline of Results using Plotly
timeline_chart = px.scatter(
result_df,
x='Compound',
y='Primary Sentiment',
color='Primary Sentiment',
hover_data=['Text'],
title='Sentiment Analysis History Timeline',
color_discrete_map={"Positive 😊": "#00C853", "Neutral 😐": "#039BE5", "Negative 😠": "#D32F2F"}
)
st.plotly_chart(timeline_chart, use_container_width=True)
# Download button for results
csv = result_df.to_csv(index=False).encode('utf-8')
st.download_button(
label="Download Results as CSV",
data=csv,
file_name='sentiment_analysis_results.csv',
mime='text/csv',
)
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