Spaces:
Sleeping
Sleeping
File size: 1,735 Bytes
53b7479 1fff55b 53b7479 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import streamlit as st
from transformers import pipeline
# Initialize the sentiment analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
# Streamlit UI
st.title('Sentiment Analysis for Customer Reviews')
# Get input text from the user
reviews_text = st.text_area("Paste customer reviews here (multiple reviews separated by a newline; recommended-upto 15 reviews at a time):", height=200)
# Button to process the sentiment
if st.button("Analyze Sentiment"):
if reviews_text:
# Split the reviews into separate lines (assuming each line is a separate review)
reviews = reviews_text.split("\n")
# Analyze the sentiment of each review
sentiment_scores = []
for review in reviews:
sentiment = sentiment_analyzer(review)[0]
sentiment_scores.append(sentiment['label'])
# Count sentiment labels
positive_count = sentiment_scores.count('POSITIVE')
negative_count = sentiment_scores.count('NEGATIVE')
neutral_count = sentiment_scores.count('NEUTRAL')
# Determine the overall sentiment
if positive_count > negative_count and positive_count > neutral_count:
overall_sentiment = 'Positive'
elif negative_count > positive_count and negative_count > neutral_count:
overall_sentiment = 'Negative'
else:
overall_sentiment = 'Neutral'
# Display results
st.subheader(f"Overall Sentiment: {overall_sentiment}")
st.write(f"Positive Reviews: {positive_count}")
st.write(f"Negative Reviews: {negative_count}")
st.write(f"Neutral Reviews: {neutral_count}")
else:
st.warning("Please paste some reviews to analyze.")
|