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#!/usr/bin/env python3
"""Prepare CSV files for analytics dashboard from original dataset.
This script generates the required CSV formats for:
1. Predictive Intervals: sentiment counts per news item
2. Category Analytics: news articles with categories
3. Thread Analysis: comments with news IDs
"""
import sys
import argparse
from pathlib import Path
import pandas as pd
import logging
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from analysis.sentiment_analyzer import SentimentAnalyzer
from data.data_loader import load_data
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def prepare_category_analytics_csv(
ria_path: str,
output_path: str = "data/analytics_category_data.csv",
max_samples: int = None
):
"""
Prepare CSV for Category Analytics tab.
Required columns: category, text
"""
logger.info(f"Loading RIA news from {ria_path}")
df = pd.read_csv(ria_path, sep='\t')
df = df[~df.tags.isnull()]
if max_samples:
df = df.head(max_samples)
# Use title + snippet as text, or just title if snippet is missing
df['text'] = df.apply(
lambda row: f"{row['title']} {row.get('snippet', '')}".strip(),
axis=1
)
# Use first tag as category (or 'unknown' if no tags)
df['category'] = df['tags'].apply(
lambda x: str(x).split(',')[0].strip() if pd.notna(x) else 'unknown'
)
# Select only required columns
output_df = df[['category', 'text']].copy()
output_df.to_csv(output_path, index=False)
logger.info(f"✅ Created category analytics CSV: {output_path} ({len(output_df)} rows)")
return output_path
def prepare_thread_analysis_csv(
vk_comments_path: str,
output_path: str = "data/analytics_thread_data.csv",
max_samples: int = None
):
"""
Prepare CSV for Thread Analysis tab.
Required columns: news_id, text
"""
logger.info(f"Loading VK comments from {vk_comments_path}")
df = pd.read_csv(vk_comments_path, sep='\t')
df = df[~df.text.isnull()]
if max_samples:
df = df.head(max_samples)
# Use post_id as news_id
df['news_id'] = df['post_id'].astype(str)
# Select only required columns
output_df = df[['news_id', 'text']].copy()
output_df.to_csv(output_path, index=False)
logger.info(f"✅ Created thread analysis CSV: {output_path} ({len(output_df)} rows)")
return output_path
def prepare_predictive_intervals_csv(
vk_comments_path: str,
vk_news_path: str = None,
output_path: str = "data/analytics_sentiment_counts.csv",
max_news_items: int = 50,
max_comments_per_item: int = 1000
):
"""
Prepare CSV for Predictive Intervals tab.
Required columns: id, positive_count, negative_count, neutral_count
This analyzes comments with sentiment to get counts per news item.
"""
logger.info(f"Loading VK comments from {vk_comments_path}")
df_comments = pd.read_csv(vk_comments_path, sep='\t')
df_comments = df_comments[~df_comments.text.isnull()]
# Limit comments per news item
df_comments = df_comments.groupby('post_id').head(max_comments_per_item)
# Get unique news items
news_ids = df_comments['post_id'].unique()[:max_news_items]
logger.info(f"Analyzing sentiment for {len(news_ids)} news items...")
# Initialize sentiment analyzer
analyzer = SentimentAnalyzer()
results = []
for i, news_id in enumerate(news_ids):
if (i + 1) % 10 == 0:
logger.info(f"Processing {i+1}/{len(news_ids)}...")
# Get comments for this news item
comments = df_comments[df_comments['post_id'] == news_id]['text'].tolist()
if not comments:
continue
# Analyze sentiment in batches
sentiments = []
batch_size = 50
for j in range(0, len(comments), batch_size):
batch = comments[j:j+batch_size]
batch_results = analyzer.analyze_batch(batch)
sentiments.extend(batch_results)
# Count sentiments
positive_count = sum(1 for s in sentiments if s.get('label') == 'POSITIVE')
negative_count = sum(1 for s in sentiments if s.get('label') == 'NEGATIVE')
neutral_count = sum(1 for s in sentiments if s.get('label') == 'NEUTRAL')
results.append({
'id': str(news_id),
'positive_count': positive_count,
'negative_count': negative_count,
'neutral_count': neutral_count
})
output_df = pd.DataFrame(results)
output_df.to_csv(output_path, index=False)
logger.info(f"✅ Created predictive intervals CSV: {output_path} ({len(output_df)} rows)")
return output_path
def main():
parser = argparse.ArgumentParser(description="Prepare CSV files for analytics dashboard")
parser.add_argument(
"--ria-path",
type=str,
default="data/news_data/ria_news.tsv",
help="Path to RIA news TSV file"
)
parser.add_argument(
"--vk-comments-path",
type=str,
default="data/news_data/vk_comments.tsv",
help="Path to VK comments TSV file"
)
parser.add_argument(
"--vk-news-path",
type=str,
default=None,
help="Path to VK news TSV file (optional)"
)
parser.add_argument(
"--output-dir",
type=str,
default="data",
help="Output directory for CSV files"
)
parser.add_argument(
"--max-samples",
type=int,
default=None,
help="Maximum samples for category analytics (for testing)"
)
parser.add_argument(
"--max-news-items",
type=int,
default=50,
help="Maximum news items for predictive intervals (sentiment analysis is slow)"
)
parser.add_argument(
"--max-comments-per-item",
type=int,
default=1000,
help="Maximum comments per news item for sentiment analysis"
)
parser.add_argument(
"--skip-sentiment",
action="store_true",
help="Skip sentiment analysis (slow step)"
)
args = parser.parse_args()
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
logger.info("=" * 60)
logger.info("Preparing Analytics Dashboard CSV Files")
logger.info("=" * 60)
# 1. Category Analytics CSV
logger.info("\n1. Preparing Category Analytics CSV...")
category_path = output_dir / "analytics_category_data.csv"
prepare_category_analytics_csv(
args.ria_path,
str(category_path),
max_samples=args.max_samples
)
# 2. Thread Analysis CSV
logger.info("\n2. Preparing Thread Analysis CSV...")
if Path(args.vk_comments_path).exists():
thread_path = output_dir / "analytics_thread_data.csv"
prepare_thread_analysis_csv(
args.vk_comments_path,
str(thread_path),
max_samples=args.max_samples
)
else:
logger.warning(f"⚠️ VK comments file not found: {args.vk_comments_path}")
logger.warning(" Skipping thread analysis CSV")
# 3. Predictive Intervals CSV (requires sentiment analysis - slow!)
if not args.skip_sentiment:
logger.info("\n3. Preparing Predictive Intervals CSV (sentiment analysis)...")
logger.info(" ⚠️ This step is slow - analyzing sentiment for comments...")
if Path(args.vk_comments_path).exists():
sentiment_path = output_dir / "analytics_sentiment_counts.csv"
prepare_predictive_intervals_csv(
args.vk_comments_path,
args.vk_news_path,
str(sentiment_path),
max_news_items=args.max_news_items,
max_comments_per_item=args.max_comments_per_item
)
else:
logger.warning(f"⚠️ VK comments file not found: {args.vk_comments_path}")
logger.warning(" Skipping predictive intervals CSV")
else:
logger.info("\n3. Skipping Predictive Intervals CSV (--skip-sentiment flag)")
logger.info("\n" + "=" * 60)
logger.info("✅ All CSV files prepared!")
logger.info("=" * 60)
logger.info("\nNext steps:")
logger.info("1. Open Streamlit analytics dashboard:")
logger.info(" streamlit run dashboards/analytics_dashboard.py")
logger.info("2. Upload the generated CSV files in each tab:")
logger.info(f" - Category Analytics: {output_dir}/analytics_category_data.csv")
if Path(args.vk_comments_path).exists():
logger.info(f" - Thread Analysis: {output_dir}/analytics_thread_data.csv")
if not args.skip_sentiment:
logger.info(f" - Predictive Intervals: {output_dir}/analytics_sentiment_counts.csv")
if __name__ == "__main__":
main()
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