| import streamlit as st
|
| import pickle
|
| import re
|
| import nltk
|
| import numpy as np
|
|
|
| from nltk.corpus import stopwords
|
| from textblob import TextBlob
|
|
|
| st.title("YouTube Comment Analysis")
|
| st.video("https://www.youtube.com/watch?v=iCvmsMzlF7o")
|
|
|
| nltk.download('stopwords')
|
| stop_words = set(stopwords.words('english'))
|
|
|
|
|
| with open('sentiment_model.pkl', 'rb') as f:
|
| model = pickle.load(f)
|
|
|
| with open('tfidf_vectorizer.pkl', 'rb') as f:
|
| vectorizer = pickle.load(f)
|
|
|
|
|
| def clean_text(text):
|
| text = text.lower()
|
| text = re.sub(r"http\S+|www\S+|https\S+", '', text)
|
| text = re.sub(r'[^a-z\s]', '', text)
|
| text = re.sub(r'\s+', ' ', text).strip()
|
| text = ' '.join([word for word in text.split() if word not in stop_words])
|
| return text
|
|
|
|
|
| st.title("🎯 YouTube Comment Sentiment Classifier")
|
|
|
| comment_input = st.text_area("Enter your YouTube comment here:")
|
|
|
| if st.button("Predict Sentiment"):
|
| if comment_input.strip() == "":
|
| st.warning("Please enter a comment.")
|
| else:
|
| cleaned = clean_text(comment_input)
|
| features = vectorizer.transform([cleaned])
|
| prediction = model.predict(features)[0]
|
|
|
| st.subheader("🔍 Sentiment Prediction:")
|
| if prediction == "Positive":
|
| st.success("😊 Positive Comment")
|
| elif prediction == "Negative":
|
| st.error("😠 Negative Comment")
|
| else:
|
| st.info("😐 Neutral Comment")
|
|
|
