| import streamlit as st |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| from textblob import TextBlob |
|
|
| def load_data(uploaded_file): |
| df = pd.read_excel(uploaded_file) |
| return df |
|
|
| def analyze_sentiment(text): |
| polarity = TextBlob(str(text)).sentiment.polarity |
| if polarity >= 0.6: |
| return "Very Positive" |
| elif polarity >= 0.2: |
| return "Positive" |
| elif polarity > -0.2: |
| return "Neutral" |
| elif polarity > -0.6: |
| return "Negative" |
| else: |
| return "Very Negative" |
|
|
| st.title("Sentiment Analysis with Pie Chart") |
|
|
| uploaded_file = st.file_uploader("Upload an Excel file with text data", type=["xlsx"]) |
|
|
| if uploaded_file is not None: |
| df = load_data(uploaded_file) |
| if "text" not in df.columns: |
| st.error("Error: The file must contain a 'text' column.") |
| else: |
| df["Sentiment"] = df["text"].apply(analyze_sentiment) |
| |
| st.write("Here is a preview of the data:") |
| st.write(df.head()) |
| |
| sentiment_counts = df["Sentiment"].value_counts() |
| |
| fig, ax = plt.subplots() |
| ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct="%1.1f%%", colors=["green", "lightgreen", "gray", "orange", "red"]) |
| ax.set_title("Sentiment Distribution") |
| st.pyplot(fig) |
| |
| csv = df.to_csv(index=False) |
| st.download_button("Download Sentiment Data", csv, "sentiment_results.csv", "text/csv") |
| else: |
| st.write("Please upload an Excel file to get started.") |
|
|
