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import streamlit as st | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
import torch | |
import re | |
import pandas as pd | |
import googleapiclient.discovery | |
import plotly.express as px | |
# Load the BERT tokenizer and model | |
tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") | |
model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") | |
# Set up the YouTube API service | |
api_service_name = "youtube" | |
api_version = "v3" | |
DEVELOPER_KEY = "AIzaSyC4Vx8G6nm3Ow9xq7NluTuCCJ1d_5w4YPE" # Replace with your actual API key | |
youtube = googleapiclient.discovery.build(api_service_name, api_version, developerKey=DEVELOPER_KEY) | |
# Function to fetch comments for a video ID | |
def scrape_comments(video_id): | |
request = youtube.commentThreads().list( | |
part="snippet", | |
videoId=video_id, | |
maxResults=100 | |
) | |
response = request.execute() | |
comments = [] | |
for item in response['items']: | |
comment = item['snippet']['topLevelComment']['snippet'] | |
comments.append([ | |
comment['textDisplay'] | |
]) | |
comments_df = pd.DataFrame(comments, columns=['comment']) | |
# df.head(10). | |
return comments_df | |
# Function to extract video ID from YouTube URL | |
def extract_video_id(video_url): | |
match = re.search(r'(?<=v=)[\w-]+', video_url) | |
if match: | |
return match.group(0) | |
else: | |
st.error("Invalid YouTube video URL") | |
# Function to fetch YouTube comments for a video ID | |
def fetch_comments(video_id): | |
# Example using youtube-comment-scraper-python library | |
comments = scrape_comments(video_id) | |
return comments | |
# Function to analyze sentiment for a single comment | |
def analyze_sentiment(comment): | |
tokens = tokenizer.encode(comment, return_tensors="pt", max_length=512, truncation=True) | |
# input_ids = tokens['input_ids'] | |
# attention_mask = tokens['attention_mask'] | |
# result = model(input_ids, attention_mask=attention_mask) | |
result = model(tokens) | |
sentiment_id = torch.argmax(result.logits) + 1 | |
if(sentiment_id > 3): | |
sentiment_label = "Positive" | |
elif(sentiment_id < 3): | |
sentiment_label = "Negative" | |
else: | |
sentiment_label = "Neutral" | |
return sentiment_label | |
def main(): | |
st.title("YouTube Comments Sentiment Analysis") | |
st.write("Enter a YouTube video link below:") | |
video_url = st.text_input("YouTube Video URL:") | |
if st.button("Extract Comments and Analyze"): | |
video_id = extract_video_id(video_url) | |
if video_id: | |
comments_df = fetch_comments(video_id) | |
# Comments is a dataframe of just the comments text | |
# st.write("Top 100 Comments extracted\n", comments_df) | |
comments_df['sentiment'] = comments_df['comment'].apply(lambda x: analyze_sentiment(x[:512])) | |
sentiment_counts = comments_df['sentiment'].value_counts() | |
positive_count = comments_df['sentiment'].value_counts().get('Positive', 0) | |
negative_count = comments_df['sentiment'].value_counts().get('Negative', 0) | |
neutral_count = comments_df['sentiment'].value_counts().get('Neutral', 0) | |
# Create pie chart in col2 with custom colors | |
fig_pie = px.pie(values=[positive_count, negative_count, neutral_count], | |
names=['Positive', 'Negative', 'Neutral'], | |
title='Pie chart representations', | |
color=sentiment_counts.index, # Use sentiment categories as colors | |
color_discrete_map={'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue'}) | |
st.plotly_chart(fig_pie, use_container_width=True) | |
# Create bar chart below the pie chart with custom colors | |
fig_bar = px.bar(x=sentiment_counts.index, y=sentiment_counts.values, | |
labels={'x': 'Sentiment', 'y': 'Count'}, | |
title='Bar plot representations', | |
color=sentiment_counts.index, # Use sentiment categories as colors | |
color_discrete_map={'Positive': 'green', 'Negative': 'red', 'Neutral': 'blue'}) | |
st.plotly_chart(fig_bar) | |
if __name__ == "__main__": | |
main() | |