Personal Knowledge Assistant

""" Social Media Intelligence and Optimization Tools This module provides comprehensive social media analysis and optimization: - Content performance analysis and optimization - Audience analysis and engagement patterns - Hashtag intelligence and trend detection - Posting time optimization - Content suggestions and competitor analysis - Cross-platform analytics and insights """ import asyncio import json import re from datetime import datetime, timedelta from typing import Dict, List, Optional, Tuple, Any, Union from dataclasses import dataclass, field from collections import defaultdict, Counter from enum import Enum import pandas as pd import numpy as np import networkx as nx from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.cluster import KMeans from sklearn.metrics.pairwise import cosine_similarity import structlog from ..models.data_models import ( SocialMediaPost, SocialPlatform, TimeRange, InsightReport ) from ..integrations.client_manager import get_client_manager from ..utils.nlp_processor import get_nlp_processor, TextAnalysisResult from ..utils.analytics_engine import get_analytics_engine, TimeSeriesPoint from ..tools.search_tools import get_search_engine from ..config.settings import get_settings logger = structlog.get_logger(__name__) class ContentType(str, Enum): """Types of social media content""" TEXT = "text" IMAGE = "image" VIDEO = "video" LINK = "link" POLL = "poll" STORY = "story" CAROUSEL = "carousel" class EngagementMetric(str, Enum): """Social media engagement metrics""" LIKES = "likes" SHARES = "shares" COMMENTS = "comments" CLICKS = "clicks" IMPRESSIONS = "impressions" REACH = "reach" SAVES = "saves" class PostingStrategy(str, Enum): """Content posting strategies""" CONSISTENT = "consistent" BURST = "burst" RESPONSIVE = "responsive" TRENDING = "trending" SEASONAL = "seasonal" @dataclass class ContentPerformance: """Performance metrics for content""" content_id: str platform: SocialPlatform content_type: ContentType engagement_rate: float reach: int impressions: int likes: int shares: int comments: int clicks: int post_time: datetime hashtags: List[str] mentions: List[str] sentiment_score: float topics: List[str] @dataclass class HashtagAnalysis: """Analysis results for hashtags""" hashtag: str usage_count: int avg_engagement: float reach_potential: int trending_score: float related_hashtags: List[str] best_platforms: List[SocialPlatform] sentiment_distribution: Dict[str, float] @dataclass class AudienceInsight: """Audience analysis insights""" platform: SocialPlatform total_followers: int engagement_rate: float active_hours: List[int] peak_days: List[int] demographics: Dict[str, Any] interests: List[Tuple[str, float]] location_distribution: Dict[str, int] device_usage: Dict[str, float] @dataclass class ContentSuggestion: """AI-generated content suggestion""" title: str content_type: ContentType suggested_text: str suggested_hashtags: List[str] best_posting_time: datetime target_platforms: List[SocialPlatform] predicted_engagement: float reasoning: str topics: List[str] confidence: float @dataclass class CompetitorAnalysis: """Competitor analysis results""" competitor_name: str platform: SocialPlatform followers_count: int avg_engagement_rate: float posting_frequency: float content_themes: List[Tuple[str, float]] top_hashtags: List[str] posting_schedule: Dict[str, List[int]] performance_benchmark: Dict[str, float] class SocialMediaAnalyzer: """Main social media intelligence analyzer""" def __init__(self): self.settings = get_settings() self.client_manager = None self.nlp_processor = None self.analytics_engine = None self.search_engine = None # Analysis configuration self._min_posts_for_analysis = 10 self._engagement_rate_threshold = 0.02 # 2% self._trending_hashtag_threshold = 100 # Caching self._performance_cache = {} self._hashtag_cache = {} self._audience_cache = {} async def initialize(self): """Initialize analyzer dependencies""" self.client_manager = await get_client_manager() self.nlp_processor = await get_nlp_processor() self.analytics_engine = get_analytics_engine() self.search_engine = await get_search_engine() async def analyze_content_performance( self, platform: SocialPlatform, time_range: TimeRange, content_types: Optional[List[ContentType]] = None ) -> Dict[str, Any]: """Analyze content performance across different dimensions""" if not self.client_manager: await self.initialize() logger.info("Starting content performance analysis", platform=platform.value) # Fetch posts from the platform posts = await self._fetch_posts(platform, time_range) if not posts: return {"error": f"No posts found for {platform.value} in the specified time range"} # Filter by content types if specified if content_types: posts = [p for p in posts if self._classify_content_type(p) in content_types] # Analyze performance performance_data = await self._analyze_post_performance(posts) results = { "platform": platform.value, "time_range": { "start": time_range.start.isoformat(), "end": time_range.end.isoformat() }, "overview": { "total_posts": len(posts), "avg_engagement_rate": performance_data["avg_engagement_rate"], "total_reach": performance_data["total_reach"], "total_impressions": performance_data["total_impressions"], "best_performing_post": performance_data["best_post"], "worst_performing_post": performance_data["worst_post"] }, "content_type_analysis": await self._analyze_by_content_type(posts), "temporal_analysis": await self._analyze_posting_times(posts), "hashtag_performance": await self._analyze_hashtag_performance(posts), "engagement_patterns": await self._analyze_engagement_patterns(posts), "recommendations": await self._generate_performance_recommendations(posts, performance_data) } return results async def analyze_hashtag_intelligence( self, hashtags: List[str], platforms: Optional[List[SocialPlatform]] = None ) -> List[HashtagAnalysis]: """Analyze hashtag performance and trends""" if not platforms: platforms = [SocialPlatform.TWITTER, SocialPlatform.LINKEDIN] hashtag_analyses = [] for hashtag in hashtags: analysis = await self._analyze_single_hashtag(hashtag, platforms) if analysis: hashtag_analyses.append(analysis) # Sort by trending score hashtag_analyses.sort(key=lambda x: x.trending_score, reverse=True) return hashtag_analyses async def optimize_posting_schedule( self, platform: SocialPlatform, time_range: TimeRange, target_metrics: Optional[List[EngagementMetric]] = None ) -> Dict[str, Any]: """Optimize posting schedule based on audience activity and engagement""" if not target_metrics: target_metrics = [EngagementMetric.LIKES, EngagementMetric.SHARES, EngagementMetric.COMMENTS] # Fetch historical posts and their performance posts = await self._fetch_posts(platform, time_range) if len(posts) < self._min_posts_for_analysis: return {"error": f"Insufficient posts ({len(posts)}) for analysis. Need at least {self._min_posts_for_analysis}."} # Analyze posting times vs engagement time_performance = await self._analyze_time_performance(posts, target_metrics) # Generate optimal schedule optimal_schedule = await self._generate_optimal_schedule(time_performance, platform) return { "platform": platform.value, "analysis_period": { "start": time_range.start.isoformat(), "end": time_range.end.isoformat(), "posts_analyzed": len(posts) }, "current_schedule_analysis": time_performance, "optimal_schedule": optimal_schedule, "predicted_improvement": optimal_schedule["predicted_improvement"], "recommendations": optimal_schedule["recommendations"] } async def analyze_audience_insights( self, platform: SocialPlatform, time_range: TimeRange ) -> AudienceInsight: """Analyze audience behavior and preferences""" # Fetch audience data from the platform audience_data = await self._fetch_audience_data(platform, time_range) # Analyze engagement patterns engagement_patterns = await self._analyze_audience_engagement(platform, time_range) # Extract insights return AudienceInsight( platform=platform, total_followers=audience_data.get("followers_count", 0), engagement_rate=engagement_patterns.get("avg_engagement_rate", 0.0), active_hours=engagement_patterns.get("peak_hours", []), peak_days=engagement_patterns.get("peak_days", []), demographics=audience_data.get("demographics", {}), interests=await self._extract_audience_interests(platform, time_range), location_distribution=audience_data.get("locations", {}), device_usage=audience_data.get("devices", {}) ) async def generate_content_suggestions( self, platform: SocialPlatform, content_themes: Optional[List[str]] = None, target_audience: Optional[Dict[str, Any]] = None, count: int = 5 ) -> List[ContentSuggestion]: """Generate AI-powered content suggestions""" if not content_themes: # Extract themes from recent high-performing content content_themes = await self._extract_trending_themes(platform) suggestions = [] for theme in content_themes[:count]: suggestion = await self._generate_content_for_theme(theme, platform, target_audience) if suggestion: suggestions.append(suggestion) # Sort by predicted engagement suggestions.sort(key=lambda x: x.predicted_engagement, reverse=True) return suggestions[:count] async def analyze_competitor_performance( self, competitor_handles: List[str], platform: SocialPlatform, time_range: TimeRange ) -> List[CompetitorAnalysis]: """Analyze competitor social media performance""" competitor_analyses = [] for handle in competitor_handles: analysis = await self._analyze_single_competitor(handle, platform, time_range) if analysis: competitor_analyses.append(analysis) return competitor_analyses async def cross_platform_analysis( self, platforms: List[SocialPlatform], time_range: TimeRange ) -> Dict[str, Any]: """Analyze performance across multiple platforms""" platform_results = {} # Analyze each platform for platform in platforms: try: platform_data = await self.analyze_content_performance(platform, time_range) platform_results[platform.value] = platform_data except Exception as e: logger.warning(f"Failed to analyze {platform.value}: {e}") platform_results[platform.value] = {"error": str(e)} # Cross-platform insights cross_insights = await self._generate_cross_platform_insights(platform_results) return { "platforms": platform_results, "cross_platform_insights": cross_insights, "recommendations": await self._generate_cross_platform_recommendations(platform_results) } # Helper methods for data fetching async def _fetch_posts( self, platform: SocialPlatform, time_range: TimeRange ) -> List[Dict[str, Any]]: """Fetch posts from a social media platform""" try: if platform == SocialPlatform.TWITTER: client = await self.client_manager.get_client('twitter') if client: return await client.get_user_tweets( start_time=time_range.start, end_time=time_range.end, max_results=200 ) elif platform == SocialPlatform.LINKEDIN: client = await self.client_manager.get_client('linkedin') if client: return await client.get_user_posts( start_time=time_range.start, end_time=time_range.end, max_results=200 ) except Exception as e: logger.error(f"Failed to fetch posts from {platform.value}: {e}") return [] async def _fetch_audience_data( self, platform: SocialPlatform, time_range: TimeRange ) -> Dict[str, Any]: """Fetch audience analytics data""" try: if platform == SocialPlatform.TWITTER: client = await self.client_manager.get_client('twitter') if client: return await client.get_audience_insights() elif platform == SocialPlatform.LINKEDIN: client = await self.client_manager.get_client('linkedin') if client: return await client.get_follower_statistics() except Exception as e: logger.error(f"Failed to fetch audience data from {platform.value}: {e}") return {} # Analysis helper methods async def _analyze_post_performance( self, posts: List[Dict[str, Any]] ) -> Dict[str, Any]: """Analyze overall performance metrics for posts""" if not posts: return {} engagement_rates = [] total_reach = 0 total_impressions = 0 best_post = None worst_post = None best_engagement = -1 worst_engagement = float('inf') for post in posts: # Calculate engagement rate likes = post.get('public_metrics', {}).get('like_count', 0) shares = post.get('public_metrics', {}).get('retweet_count', 0) comments = post.get('public_metrics', {}).get('reply_count', 0) impressions = post.get('public_metrics', {}).get('impression_count', 1) engagement_rate = (likes + shares + comments) / max(impressions, 1) engagement_rates.append(engagement_rate) total_reach += post.get('public_metrics', {}).get('impression_count', 0) total_impressions += impressions # Track best and worst performing posts if engagement_rate > best_engagement: best_engagement = engagement_rate best_post = { 'id': post.get('id'), 'text': post.get('text', '')[:100] + '...', 'engagement_rate': engagement_rate, 'created_at': post.get('created_at') } if engagement_rate < worst_engagement: worst_engagement = engagement_rate worst_post = { 'id': post.get('id'), 'text': post.get('text', '')[:100] + '...', 'engagement_rate': engagement_rate, 'created_at': post.get('created_at') } return { 'avg_engagement_rate': np.mean(engagement_rates) if engagement_rates else 0.0, 'total_reach': total_reach, 'total_impressions': total_impressions, 'best_post': best_post, 'worst_post': worst_post, 'engagement_distribution': { 'mean': float(np.mean(engagement_rates)) if engagement_rates else 0.0, 'median': float(np.median(engagement_rates)) if engagement_rates else 0.0, 'std': float(np.std(engagement_rates)) if engagement_rates else 0.0 } } async def _analyze_by_content_type( self, posts: List[Dict[str, Any]] ) -> Dict[str, Any]: """Analyze performance by content type""" content_type_performance = defaultdict(list) for post in posts: content_type = self._classify_content_type(post) # Calculate engagement metrics likes = post.get('public_metrics', {}).get('like_count', 0) shares = post.get('public_metrics', {}).get('retweet_count', 0) comments = post.get('public_metrics', {}).get('reply_count', 0) impressions = post.get('public_metrics', {}).get('impression_count', 1) engagement_rate = (likes + shares + comments) / max(impressions, 1) content_type_performance[content_type.value].append({ 'engagement_rate': engagement_rate, 'likes': likes, 'shares': shares, 'comments': comments, 'impressions': impressions }) # Calculate averages for each content type results = {} for content_type, metrics_list in content_type_performance.items(): if metrics_list: results[content_type] = { 'count': len(metrics_list), 'avg_engagement_rate': np.mean([m['engagement_rate'] for m in metrics_list]), 'avg_likes': np.mean([m['likes'] for m in metrics_list]), 'avg_shares': np.mean([m['shares'] for m in metrics_list]), 'avg_comments': np.mean([m['comments'] for m in metrics_list]), 'total_impressions': sum([m['impressions'] for m in metrics_list]) } return results def _classify_content_type(self, post: Dict[str, Any]) -> ContentType: """Classify the type of content in a post""" # Check for media attachments if post.get('attachments'): attachments = post['attachments'] if 'media_keys' in attachments: # This would need to be enhanced with actual media type detection return ContentType.IMAGE # Check for URLs (link posts) text = post.get('text', '') if 'http' in text or 'www.' in text: return ContentType.LINK # Check for polls (platform-specific) if post.get('attachments', {}).get('poll_ids'): return ContentType.POLL # Default to text return ContentType.TEXT async def _analyze_posting_times( self, posts: List[Dict[str, Any]] ) -> Dict[str, Any]: """Analyze optimal posting times""" if not posts: return {} # Extract posting times and engagement time_engagement = [] for post in posts: created_at = post.get('created_at') if created_at: if isinstance(created_at, str): timestamp = pd.to_datetime(created_at) else: timestamp = created_at # Calculate engagement likes = post.get('public_metrics', {}).get('like_count', 0) shares = post.get('public_metrics', {}).get('retweet_count', 0) comments = post.get('public_metrics', {}).get('reply_count', 0) engagement = likes + shares + comments time_engagement.append({ 'hour': timestamp.hour, 'day_of_week': timestamp.dayofweek, 'engagement': engagement, 'timestamp': timestamp }) if not time_engagement: return {} # Analyze by hour hourly_performance = defaultdict(list) for item in time_engagement: hourly_performance[item['hour']].append(item['engagement']) hourly_avg = { hour: np.mean(engagements) for hour, engagements in hourly_performance.items() } # Analyze by day of week daily_performance = defaultdict(list) for item in time_engagement: daily_performance[item['day_of_week']].append(item['engagement']) daily_avg = { day: np.mean(engagements) for day, engagements in daily_performance.items() } # Find optimal times best_hours = sorted(hourly_avg.items(), key=lambda x: x[1], reverse=True)[:3] best_days = sorted(daily_avg.items(), key=lambda x: x[1], reverse=True)[:3] return { 'hourly_performance': dict(hourly_avg), 'daily_performance': dict(daily_avg), 'best_hours': [hour for hour, _ in best_hours], 'best_days': [day for day, _ in best_days], 'recommendations': { 'optimal_posting_hours': [hour for hour, _ in best_hours], 'optimal_posting_days': [day for day, _ in best_days] } } async def _analyze_hashtag_performance( self, posts: List[Dict[str, Any]] ) -> Dict[str, Any]: """Analyze hashtag performance""" hashtag_performance = defaultdict(list) for post in posts: # Extract hashtags hashtags = self._extract_hashtags(post.get('text', '')) if hashtags: # Calculate engagement likes = post.get('public_metrics', {}).get('like_count', 0) shares = post.get('public_metrics', {}).get('retweet_count', 0) comments = post.get('public_metrics', {}).get('reply_count', 0) engagement = likes + shares + comments for hashtag in hashtags: hashtag_performance[hashtag].append(engagement) # Calculate averages hashtag_stats = {} for hashtag, engagements in hashtag_performance.items(): if len(engagements) >= 2: # Only include hashtags used multiple times hashtag_stats[hashtag] = { 'usage_count': len(engagements), 'avg_engagement': np.mean(engagements), 'total_engagement': sum(engagements), 'engagement_variance': np.var(engagements) } # Sort by average engagement top_hashtags = sorted( hashtag_stats.items(), key=lambda x: x[1]['avg_engagement'], reverse=True )[:10] return { 'total_unique_hashtags': len(hashtag_performance), 'hashtags_with_multiple_uses': len(hashtag_stats), 'top_performing_hashtags': dict(top_hashtags), 'hashtag_usage_distribution': { hashtag: stats['usage_count'] for hashtag, stats in hashtag_stats.items() } } def _extract_hashtags(self, text: str) -> List[str]: """Extract hashtags from text""" hashtag_pattern = r'#(\w+)' hashtags = re.findall(hashtag_pattern, text.lower()) return [f"#{tag}" for tag in hashtags] async def _analyze_engagement_patterns( self, posts: List[Dict[str, Any]] ) -> Dict[str, Any]: """Analyze detailed engagement patterns""" engagement_data = [] for post in posts: metrics = post.get('public_metrics', {}) engagement_data.append({ 'likes': metrics.get('like_count', 0), 'shares': metrics.get('retweet_count', 0), 'comments': metrics.get('reply_count', 0), 'impressions': metrics.get('impression_count', 0), 'created_at': post.get('created_at') }) if not engagement_data: return {} # Calculate engagement ratios like_ratios = [] share_ratios = [] comment_ratios = [] for data in engagement_data: total_engagement = data['likes'] + data['shares'] + data['comments'] if total_engagement > 0: like_ratios.append(data['likes'] / total_engagement) share_ratios.append(data['shares'] / total_engagement) comment_ratios.append(data['comments'] / total_engagement) return { 'engagement_composition': { 'avg_like_ratio': np.mean(like_ratios) if like_ratios else 0.0, 'avg_share_ratio': np.mean(share_ratios) if share_ratios else 0.0, 'avg_comment_ratio': np.mean(comment_ratios) if comment_ratios else 0.0 }, 'engagement_trends': await self._calculate_engagement_trends(engagement_data), 'engagement_velocity': await self._calculate_engagement_velocity(engagement_data) } async def _calculate_engagement_trends( self, engagement_data: List[Dict[str, Any]] ) -> Dict[str, Any]: """Calculate engagement trends over time""" if len(engagement_data) < 3: return {} # Sort by date sorted_data = sorted(engagement_data, key=lambda x: x['created_at']) # Extract time series data timestamps = [item['created_at'] for item in sorted_data] total_engagements = [ item['likes'] + item['shares'] + item['comments'] for item in sorted_data ] # Use analytics engine for trend analysis time_series_points = [ TimeSeriesPoint(timestamp=ts, value=eng) for ts, eng in zip(timestamps, total_engagements) ] trend_analysis = await self.analytics_engine.analyze_time_series( time_series_points, detect_seasonality=False, detect_anomalies=False ) return trend_analysis.get('trend', {}) async def _calculate_engagement_velocity( self, engagement_data: List[Dict[str, Any]] ) -> Dict[str, Any]: """Calculate how quickly posts gain engagement""" # This would require time-series engagement data # For now, return placeholder return { 'avg_time_to_peak': 3600, # 1 hour in seconds 'engagement_half_life': 7200, # 2 hours in seconds 'velocity_score': 0.75 } async def _generate_performance_recommendations( self, posts: List[Dict[str, Any]], performance_data: Dict[str, Any] ) -> List[str]: """Generate actionable recommendations based on performance analysis""" recommendations = [] # Engagement rate recommendations avg_engagement = performance_data.get('avg_engagement_rate', 0.0) if avg_engagement < self._engagement_rate_threshold: recommendations.append( f"Your average engagement rate ({avg_engagement:.2%}) is below the typical threshold " f"({self._engagement_rate_threshold:.2%}). Consider experimenting with different content types and posting times." ) # Content type recommendations content_analysis = await self._analyze_by_content_type(posts) if content_analysis: best_type = max(content_analysis.items(), key=lambda x: x[1]['avg_engagement_rate']) recommendations.append( f"Your {best_type[0]} content performs best with {best_type[1]['avg_engagement_rate']:.2%} " f"average engagement. Consider creating more {best_type[0]} content." ) # Posting time recommendations time_analysis = await self._analyze_posting_times(posts) if time_analysis.get('best_hours'): best_hours = time_analysis['best_hours'] recommendations.append( f"Your posts perform best at {', '.join(map(str, best_hours))}:00. " f"Consider scheduling important content during these hours." ) return recommendations async def _analyze_single_hashtag( self, hashtag: str, platforms: List[SocialPlatform] ) -> Optional[HashtagAnalysis]: """Analyze a single hashtag across platforms""" try: # This would integrate with actual hashtag tracking APIs # For now, return placeholder analysis return HashtagAnalysis( hashtag=hashtag, usage_count=1500, avg_engagement=125.5, reach_potential=15000, trending_score=0.75, related_hashtags=[f"#{hashtag}related", f"#{hashtag}trend"], best_platforms=[SocialPlatform.TWITTER], sentiment_distribution={"positive": 0.6, "neutral": 0.3, "negative": 0.1} ) except Exception as e: logger.error(f"Failed to analyze hashtag {hashtag}: {e}") return None async def _analyze_time_performance( self, posts: List[Dict[str, Any]], target_metrics: List[EngagementMetric] ) -> Dict[str, Any]: """Analyze posting time vs performance""" time_performance = defaultdict(list) for post in posts: created_at = post.get('created_at') if created_at: if isinstance(created_at, str): timestamp = pd.to_datetime(created_at) else: timestamp = created_at hour = timestamp.hour day_of_week = timestamp.dayofweek # Calculate target metrics metrics = {} for metric in target_metrics: if metric == EngagementMetric.LIKES: metrics['likes'] = post.get('public_metrics', {}).get('like_count', 0) elif metric == EngagementMetric.SHARES: metrics['shares'] = post.get('public_metrics', {}).get('retweet_count', 0) elif metric == EngagementMetric.COMMENTS: metrics['comments'] = post.get('public_metrics', {}).get('reply_count', 0) time_key = f"{day_of_week}_{hour}" time_performance[time_key].append(metrics) # Calculate averages time_averages = {} for time_key, metrics_list in time_performance.items(): if metrics_list: avg_metrics = {} for metric in target_metrics: metric_name = metric.value values = [m.get(metric_name, 0) for m in metrics_list] avg_metrics[metric_name] = np.mean(values) if values else 0.0 time_averages[time_key] = { 'count': len(metrics_list), 'averages': avg_metrics, 'total_score': sum(avg_metrics.values()) } return time_averages async def _generate_optimal_schedule( self, time_performance: Dict[str, Any], platform: SocialPlatform ) -> Dict[str, Any]: """Generate optimal posting schedule""" # Sort time slots by performance sorted_times = sorted( time_performance.items(), key=lambda x: x[1]['total_score'], reverse=True ) # Extract best times best_times = [] for time_key, data in sorted_times[:7]: # Top 7 time slots day_of_week, hour = map(int, time_key.split('_')) best_times.append({ 'day_of_week': day_of_week, 'hour': hour, 'expected_performance': data['total_score'], 'confidence': min(1.0, data['count'] / 10.0) # Based on data points }) # Calculate predicted improvement current_avg = np.mean([data['total_score'] for data in time_performance.values()]) optimal_avg = np.mean([time['expected_performance'] for time in best_times[:3]]) improvement = (optimal_avg - current_avg) / current_avg if current_avg > 0 else 0.0 return { 'optimal_time_slots': best_times, 'recommended_frequency': self._calculate_optimal_frequency(platform), 'predicted_improvement': improvement, 'recommendations': [ f"Post during your top 3 time slots for {improvement:.1%} improvement in engagement", f"Maintain consistent posting frequency of {self._calculate_optimal_frequency(platform)} posts per week" ] } def _calculate_optimal_frequency(self, platform: SocialPlatform) -> int: """Calculate optimal posting frequency for platform""" # Platform-specific recommendations if platform == SocialPlatform.TWITTER: return 7 # Daily elif platform == SocialPlatform.LINKEDIN: return 3 # 3 times per week else: return 5 # Default async def _analyze_audience_engagement( self, platform: SocialPlatform, time_range: TimeRange ) -> Dict[str, Any]: """Analyze audience engagement patterns""" # This would integrate with platform analytics APIs # Placeholder implementation return { 'avg_engagement_rate': 0.045, # 4.5% 'peak_hours': [9, 14, 20], 'peak_days': [1, 2, 3], # Monday, Tuesday, Wednesday 'engagement_by_content_type': { 'text': 0.03, 'image': 0.05, 'video': 0.07 } } async def _extract_audience_interests( self, platform: SocialPlatform, time_range: TimeRange ) -> List[Tuple[str, float]]: """Extract audience interests from engagement data""" # This would analyze what content gets the most engagement # Placeholder implementation return [ ("technology", 0.85), ("business", 0.72), ("innovation", 0.68), ("productivity", 0.61), ("leadership", 0.55) ] async def _extract_trending_themes( self, platform: SocialPlatform ) -> List[str]: """Extract trending themes for content generation""" # This would analyze current trends and high-performing content return [ "AI and machine learning", "Remote work productivity", "Digital transformation", "Sustainable business practices", "Personal branding" ] async def _generate_content_for_theme( self, theme: str, platform: SocialPlatform, target_audience: Optional[Dict[str, Any]] ) -> Optional[ContentSuggestion]: """Generate content suggestion for a specific theme""" try: # Use NLP processor to generate content ideas keywords = await self.nlp_processor._extract_keywords(theme, max_keywords=3) # Generate content based on theme and platform if platform == SocialPlatform.TWITTER: suggested_text = f"Exploring {theme}: Key insights and trends shaping the future. What's your take? 🤔" content_type = ContentType.TEXT elif platform == SocialPlatform.LINKEDIN: suggested_text = f"The impact of {theme} on modern business cannot be overstated. Here's why it matters for your organization..." content_type = ContentType.TEXT else: suggested_text = f"Let's discuss {theme} and its implications." content_type = ContentType.TEXT # Generate hashtags suggested_hashtags = [f"#{keyword[0].replace(' ', '')}" for keyword, _ in keywords[:3]] suggested_hashtags.append(f"#{theme.replace(' ', '')}") # Predict optimal posting time (placeholder) optimal_time = datetime.now() + timedelta(hours=2) return ContentSuggestion( title=f"Content about {theme}", content_type=content_type, suggested_text=suggested_text, suggested_hashtags=suggested_hashtags, best_posting_time=optimal_time, target_platforms=[platform], predicted_engagement=0.035, # 3.5% predicted engagement reasoning=f"This theme aligns with current trends and your audience interests", topics=[theme], confidence=0.75 ) except Exception as e: logger.error(f"Failed to generate content for theme {theme}: {e}") return None async def _analyze_single_competitor( self, handle: str, platform: SocialPlatform, time_range: TimeRange ) -> Optional[CompetitorAnalysis]: """Analyze a single competitor's performance""" try: # This would integrate with social media APIs to fetch competitor data # Placeholder implementation return CompetitorAnalysis( competitor_name=handle, platform=platform, followers_count=50000, avg_engagement_rate=0.042, posting_frequency=5.2, # posts per week content_themes=[ ("technology", 0.35), ("business strategy", 0.28), ("industry news", 0.22) ], top_hashtags=["#tech", "#business", "#innovation"], posting_schedule={ "Monday": [9, 14], "Tuesday": [10, 15], "Wednesday": [9, 16], "Thursday": [11, 14], "Friday": [10, 13] }, performance_benchmark={ "engagement_rate": 0.042, "avg_likes": 250, "avg_shares": 45, "avg_comments": 18 } ) except Exception as e: logger.error(f"Failed to analyze competitor {handle}: {e}") return None async def _generate_cross_platform_insights( self, platform_results: Dict[str, Any] ) -> Dict[str, Any]: """Generate insights from cross-platform analysis""" insights = { "performance_comparison": {}, "content_type_preferences": {}, "optimal_cross_posting": {}, "platform_specific_recommendations": {} } # Compare performance across platforms for platform, data in platform_results.items(): if "error" not in data: overview = data.get("overview", {}) insights["performance_comparison"][platform] = { "engagement_rate": overview.get("avg_engagement_rate", 0.0), "total_reach": overview.get("total_reach", 0), "posts_count": overview.get("total_posts", 0) } return insights async def _generate_cross_platform_recommendations( self, platform_results: Dict[str, Any] ) -> List[str]: """Generate recommendations for cross-platform strategy""" recommendations = [] # Find best performing platform best_platform = None best_engagement = 0.0 for platform, data in platform_results.items(): if "error" not in data: engagement = data.get("overview", {}).get("avg_engagement_rate", 0.0) if engagement > best_engagement: best_engagement = engagement best_platform = platform if best_platform: recommendations.append( f"Focus more effort on {best_platform} where you have the highest engagement rate ({best_engagement:.2%})" ) # Content adaptation recommendations recommendations.append( "Adapt content format for each platform - longer posts for LinkedIn, concise posts for Twitter" ) recommendations.append( "Cross-promote your best performing content across platforms with platform-specific adaptations" ) return recommendations # Global analyzer instance _social_analyzer: Optional[SocialMediaAnalyzer] = None async def get_social_analyzer() -> SocialMediaAnalyzer: """Get the global social media analyzer instance""" global _social_analyzer if _social_analyzer is None: _social_analyzer = SocialMediaAnalyzer() await _social_analyzer.initialize() return _social_analyzer async def quick_content_performance_analysis( platform: SocialPlatform, days_back: int = 30 ) -> Dict[str, Any]: """Quick content performance analysis for the last N days""" analyzer = await get_social_analyzer() end_date = datetime.now() start_date = end_date - timedelta(days=days_back) time_range = TimeRange(start=start_date, end=end_date) return await analyzer.analyze_content_performance(platform, time_range) async def generate_content_ideas( platform: SocialPlatform, themes: Optional[List[str]] = None, count: int = 3 ) -> List[ContentSuggestion]: """Generate content ideas for a platform""" analyzer = await get_social_analyzer() return await analyzer.generate_content_suggestions(platform, themes, count=count) async def optimize_posting_times( platform: SocialPlatform, days_back: int = 60 ) -> Dict[str, Any]: """Optimize posting schedule based on recent performance""" analyzer = await get_social_analyzer() end_date = datetime.now() start_date = end_date - timedelta(days=days_back) time_range = TimeRange(start=start_date, end=end_date) return await analyzer.optimize_posting_schedule(platform, time_range)