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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() | |