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"""
Advanced Research Trend Monitor - Web App Version
Based on the notebook implementation with enhanced features
"""
import json
import time
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from collections import defaultdict, Counter
import re
# Optional imports for advanced features
try:
import networkx as nx
HAS_NETWORKX = True
except ImportError:
HAS_NETWORKX = False
print("⚠️ NetworkX not available - some advanced features disabled")
try:
import matplotlib.pyplot as plt
import seaborn as sns
HAS_PLOTTING = True
except ImportError:
HAS_PLOTTING = False
print("⚠️ Matplotlib/Seaborn not available - plotting features disabled")
try:
from wordcloud import WordCloud
HAS_WORDCLOUD = True
except ImportError:
HAS_WORDCLOUD = False
print("⚠️ WordCloud not available - word cloud features disabled")
try:
import numpy as np
HAS_NUMPY = True
except ImportError:
HAS_NUMPY = False
print("⚠️ NumPy not available - some numerical features disabled")
class AdvancedTrendMonitor:
"""Advanced research trend monitoring with temporal analysis and gap detection"""
def __init__(self, groq_processor=None):
self.groq_processor = groq_processor
self.trend_data = {}
self.keyword_trends = defaultdict(list)
self.temporal_data = defaultdict(list)
self.gap_analysis_cache = {}
print("✅ Advanced Research Trend Monitor initialized!")
def analyze_temporal_trends(self, papers: List[Dict], timeframe: str = "yearly") -> Dict:
"""Analyze trends over time with sophisticated temporal analysis"""
try:
if not papers:
return {'error': 'No papers provided for temporal analysis'}
# Group papers by time period
temporal_groups = defaultdict(list)
year_counts = defaultdict(int)
keyword_evolution = defaultdict(lambda: defaultdict(int))
for paper in papers:
year = paper.get('year')
if not year:
continue
# Handle different year formats
if isinstance(year, str):
try:
year = int(year)
except ValueError:
continue
if year < 1990 or year > 2030: # Filter unrealistic years
continue
temporal_groups[year].append(paper)
year_counts[year] += 1
# Track keyword evolution
title = paper.get('title', '').lower()
abstract = paper.get('abstract', '').lower()
content = f"{title} {abstract}"
# Extract keywords (simple approach)
keywords = self._extract_keywords(content)
for keyword in keywords:
keyword_evolution[year][keyword] += 1
# Calculate trends
trends = {
'publication_trend': dict(sorted(year_counts.items())),
'keyword_evolution': dict(keyword_evolution),
'temporal_analysis': {},
'growth_analysis': {},
'emerging_topics': {},
'declining_topics': {}
}
# Analyze publication growth
years = sorted(year_counts.keys())
if len(years) >= 2:
recent_years = years[-3:] # Last 3 years
earlier_years = years[:-3] if len(years) > 3 else years[:-1]
recent_avg = sum(year_counts[y] for y in recent_years) / len(recent_years)
earlier_avg = sum(year_counts[y] for y in earlier_years) / len(earlier_years) if earlier_years else 0
growth_rate = ((recent_avg - earlier_avg) / earlier_avg * 100) if earlier_avg > 0 else 0
trends['growth_analysis'] = {
'recent_average': recent_avg,
'earlier_average': earlier_avg,
'growth_rate_percent': growth_rate,
'trend_direction': 'growing' if growth_rate > 5 else 'declining' if growth_rate < -5 else 'stable'
}
# Analyze emerging vs declining topics
if len(years) >= 2:
recent_year = years[-1]
previous_year = years[-2] if len(years) > 1 else years[-1]
recent_keywords = set(keyword_evolution[recent_year].keys())
previous_keywords = set(keyword_evolution[previous_year].keys())
emerging = recent_keywords - previous_keywords
declining = previous_keywords - recent_keywords
trends['emerging_topics'] = {
'topics': list(emerging)[:10], # Top 10 emerging
'count': len(emerging)
}
trends['declining_topics'] = {
'topics': list(declining)[:10], # Top 10 declining
'count': len(declining)
}
# Temporal analysis summary
trends['temporal_analysis'] = {
'total_years': len(years),
'year_range': f"{min(years)}-{max(years)}" if years else "N/A",
'peak_year': max(year_counts.items(), key=lambda x: x[1])[0] if year_counts else None,
'total_papers': sum(year_counts.values()),
'average_per_year': sum(year_counts.values()) / len(years) if years else 0
}
return trends
except Exception as e:
return {
'error': f'Temporal trend analysis failed: {str(e)}',
'analysis_timestamp': datetime.now().isoformat()
}
def detect_research_gaps(self, papers: List[Dict]) -> Dict:
"""Detect research gaps using advanced analysis"""
try:
if not papers:
return {'error': 'No papers provided for gap analysis'}
# Analyze methodologies
methodologies = defaultdict(int)
research_areas = defaultdict(int)
data_types = defaultdict(int)
evaluation_methods = defaultdict(int)
# Common research area keywords
area_keywords = {
'natural_language_processing': ['nlp', 'language', 'text', 'linguistic'],
'computer_vision': ['vision', 'image', 'visual', 'cv'],
'machine_learning': ['ml', 'learning', 'algorithm', 'model'],
'deep_learning': ['deep', 'neural', 'network', 'cnn', 'rnn'],
'reinforcement_learning': ['reinforcement', 'rl', 'agent', 'policy'],
'robotics': ['robot', 'robotic', 'manipulation', 'control'],
'healthcare': ['medical', 'health', 'clinical', 'patient'],
'finance': ['financial', 'trading', 'market', 'economic'],
'security': ['security', 'privacy', 'attack', 'defense']
}
# Methodology keywords
method_keywords = {
'supervised_learning': ['supervised', 'classification', 'regression'],
'unsupervised_learning': ['unsupervised', 'clustering', 'dimensionality'],
'semi_supervised': ['semi-supervised', 'few-shot', 'zero-shot'],
'transfer_learning': ['transfer', 'domain adaptation', 'fine-tuning'],
'federated_learning': ['federated', 'distributed', 'decentralized'],
'meta_learning': ['meta', 'learning to learn', 'few-shot'],
'explainable_ai': ['explainable', 'interpretable', 'explanation'],
'adversarial': ['adversarial', 'robust', 'attack']
}
# Analyze papers
for paper in papers:
content = f"{paper.get('title', '')} {paper.get('abstract', '')}".lower()
# Count research areas
for area, keywords in area_keywords.items():
if any(keyword in content for keyword in keywords):
research_areas[area] += 1
# Count methodologies
for method, keywords in method_keywords.items():
if any(keyword in content for keyword in keywords):
methodologies[method] += 1
# Identify data types
if 'dataset' in content or 'data' in content:
if any(word in content for word in ['text', 'corpus', 'language']):
data_types['text'] += 1
elif any(word in content for word in ['image', 'visual', 'video']):
data_types['image'] += 1
elif any(word in content for word in ['audio', 'speech', 'sound']):
data_types['audio'] += 1
elif any(word in content for word in ['sensor', 'iot', 'time series']):
data_types['sensor'] += 1
else:
data_types['tabular'] += 1
# Identify gaps
gaps = {
'methodology_gaps': [],
'research_area_gaps': [],
'data_type_gaps': [],
'interdisciplinary_gaps': [],
'emerging_gaps': []
}
# Find underexplored methodologies
total_papers = len(papers)
for method, count in methodologies.items():
coverage = (count / total_papers) * 100
if coverage < 5: # Less than 5% coverage
gaps['methodology_gaps'].append({
'method': method.replace('_', ' ').title(),
'coverage_percent': coverage,
'papers_count': count
})
# Find underexplored research areas
for area, count in research_areas.items():
coverage = (count / total_papers) * 100
if coverage < 10: # Less than 10% coverage
gaps['research_area_gaps'].append({
'area': area.replace('_', ' ').title(),
'coverage_percent': coverage,
'papers_count': count
})
# Find underexplored data types
for dtype, count in data_types.items():
coverage = (count / total_papers) * 100
if coverage < 15: # Less than 15% coverage
gaps['data_type_gaps'].append({
'data_type': dtype.replace('_', ' ').title(),
'coverage_percent': coverage,
'papers_count': count
})
# Generate AI-powered gap analysis
if self.groq_processor:
ai_analysis = self._generate_ai_gap_analysis(papers, gaps)
gaps['ai_analysis'] = ai_analysis
gaps['analysis_summary'] = {
'total_papers_analyzed': total_papers,
'methodology_gaps_found': len(gaps['methodology_gaps']),
'research_area_gaps_found': len(gaps['research_area_gaps']),
'data_type_gaps_found': len(gaps['data_type_gaps']),
'analysis_timestamp': datetime.now().isoformat()
}
return gaps
except Exception as e:
return {
'error': f'Gap detection failed: {str(e)}',
'analysis_timestamp': datetime.now().isoformat()
}
def generate_trend_report(self, papers: List[Dict]) -> Dict:
"""Generate comprehensive trend report"""
try:
if not papers:
return {'error': 'No papers provided for trend report'}
print(f"📊 Generating trend report for {len(papers)} papers...")
# Run all analyses
temporal_trends = self.analyze_temporal_trends(papers)
research_gaps = self.detect_research_gaps(papers)
# Generate keyword trends
keyword_analysis = self._analyze_keyword_trends(papers)
# Generate emerging topics
emerging_topics = self._detect_emerging_topics(papers)
# Generate AI-powered executive summary
executive_summary = self._generate_executive_summary(papers, temporal_trends, research_gaps)
# Compile comprehensive report
report = {
'executive_summary': executive_summary,
'temporal_trends': temporal_trends,
'research_gaps': research_gaps,
'keyword_analysis': keyword_analysis,
'emerging_topics': emerging_topics,
'report_metadata': {
'papers_analyzed': len(papers),
'analysis_date': datetime.now().isoformat(),
'report_version': '2.0'
}
}
return report
except Exception as e:
return {
'error': f'Trend report generation failed: {str(e)}',
'analysis_timestamp': datetime.now().isoformat()
}
def _extract_keywords(self, content: str) -> List[str]:
"""Extract keywords from content using simple NLP"""
# Remove common words and extract meaningful terms
stop_words = {'the', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'a', 'an', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should', 'may', 'might', 'must', 'shall', 'can', 'this', 'that', 'these', 'those', 'we', 'they', 'our', 'their', 'using', 'based', 'approach', 'method', 'model', 'paper', 'study', 'research', 'work', 'results', 'show', 'propose', 'present'}
# Extract words (simple tokenization)
words = re.findall(r'\b[a-zA-Z]+\b', content.lower())
# Filter keywords
keywords = [word for word in words if len(word) > 3 and word not in stop_words]
# Return top keywords
return list(Counter(keywords).keys())[:20]
def _analyze_keyword_trends(self, papers: List[Dict]) -> Dict:
"""Analyze keyword trends over time"""
try:
keyword_by_year = defaultdict(lambda: defaultdict(int))
for paper in papers:
year = paper.get('year')
if not year:
continue
content = f"{paper.get('title', '')} {paper.get('abstract', '')}".lower()
keywords = self._extract_keywords(content)
for keyword in keywords[:10]: # Top 10 keywords per paper
keyword_by_year[year][keyword] += 1
# Find trending keywords
trending_keywords = {}
for keyword in set().union(*[keywords.keys() for keywords in keyword_by_year.values()]):
years = sorted(keyword_by_year.keys())
if len(years) >= 2:
recent_count = keyword_by_year[years[-1]][keyword]
previous_count = keyword_by_year[years[-2]][keyword]
if previous_count > 0:
trend = ((recent_count - previous_count) / previous_count) * 100
trending_keywords[keyword] = trend
# Get top trending keywords
top_trending = sorted(trending_keywords.items(), key=lambda x: x[1], reverse=True)[:10]
return {
'keyword_evolution': dict(keyword_by_year),
'trending_keywords': top_trending,
'analysis_timestamp': datetime.now().isoformat()
}
except Exception as e:
return {
'error': f'Keyword trend analysis failed: {str(e)}',
'analysis_timestamp': datetime.now().isoformat()
}
def _detect_emerging_topics(self, papers: List[Dict]) -> Dict:
"""Detect emerging research topics"""
try:
# Group papers by recent years
recent_papers = []
older_papers = []
current_year = datetime.now().year
for paper in papers:
year = paper.get('year')
if not year:
continue
if isinstance(year, str):
try:
year = int(year)
except ValueError:
continue
if year >= current_year - 2: # Last 2 years
recent_papers.append(paper)
else:
older_papers.append(paper)
# Extract topics from recent vs older papers
recent_topics = set()
older_topics = set()
for paper in recent_papers:
content = f"{paper.get('title', '')} {paper.get('abstract', '')}".lower()
topics = self._extract_keywords(content)
recent_topics.update(topics[:5]) # Top 5 topics per paper
for paper in older_papers:
content = f"{paper.get('title', '')} {paper.get('abstract', '')}".lower()
topics = self._extract_keywords(content)
older_topics.update(topics[:5])
# Find emerging topics (in recent but not in older)
emerging = recent_topics - older_topics
return {
'emerging_topics': list(emerging)[:15], # Top 15 emerging topics
'recent_papers_count': len(recent_papers),
'older_papers_count': len(older_papers),
'analysis_timestamp': datetime.now().isoformat()
}
except Exception as e:
return {
'error': f'Emerging topic detection failed: {str(e)}',
'analysis_timestamp': datetime.now().isoformat()
}
def _generate_ai_gap_analysis(self, papers: List[Dict], gaps: Dict) -> str:
"""Generate AI-powered gap analysis"""
try:
if not self.groq_processor:
return "AI analysis not available - Groq processor not initialized"
# Prepare summary for AI analysis
summary = f"""
Research Gap Analysis Summary:
- Total Papers Analyzed: {len(papers)}
- Methodology Gaps Found: {len(gaps['methodology_gaps'])}
- Research Area Gaps Found: {len(gaps['research_area_gaps'])}
- Data Type Gaps Found: {len(gaps['data_type_gaps'])}
Top Methodology Gaps:
{', '.join([gap['method'] for gap in gaps['methodology_gaps'][:5]])}
Top Research Area Gaps:
{', '.join([gap['area'] for gap in gaps['research_area_gaps'][:5]])}
"""
prompt = f"""Based on this research gap analysis, provide insights on:
{summary}
Please provide:
1. **Key Research Gaps**: Most significant gaps and why they matter
2. **Opportunities**: Potential research opportunities in underexplored areas
3. **Recommendations**: Specific recommendations for future research
4. **Priority Areas**: Which gaps should be prioritized and why
Format as a structured analysis."""
response = self.groq_processor.generate_response(prompt, max_tokens=1500)
return response
except Exception as e:
return f"AI gap analysis failed: {str(e)}"
def _generate_executive_summary(self, papers: List[Dict], temporal_trends: Dict, research_gaps: Dict) -> str:
"""Generate executive summary of trend analysis"""
try:
if not self.groq_processor:
return "Executive summary not available - Groq processor not initialized"
# Prepare data for summary
growth_info = temporal_trends.get('growth_analysis', {})
gap_summary = research_gaps.get('analysis_summary', {})
prompt = f"""Generate an executive summary for this research trend analysis:
Papers Analyzed: {len(papers)}
Publication Growth: {growth_info.get('trend_direction', 'unknown')} ({growth_info.get('growth_rate_percent', 0):.1f}%)
Research Gaps Found: {gap_summary.get('methodology_gaps_found', 0)} methodology gaps, {gap_summary.get('research_area_gaps_found', 0)} area gaps
Temporal Analysis:
- Year Range: {temporal_trends.get('temporal_analysis', {}).get('year_range', 'N/A')}
- Peak Year: {temporal_trends.get('temporal_analysis', {}).get('peak_year', 'N/A')}
- Average Papers/Year: {temporal_trends.get('temporal_analysis', {}).get('average_per_year', 0):.1f}
Provide a 3-paragraph executive summary covering:
1. Overall research landscape and trends
2. Key findings and patterns
3. Implications and future directions"""
response = self.groq_processor.generate_response(prompt, max_tokens=1000)
return response
except Exception as e:
return f"Executive summary generation failed: {str(e)}"
def get_trend_summary(self) -> Dict:
"""Get summary of all trend data"""
return {
'total_trends_tracked': len(self.trend_data),
'keyword_trends_count': len(self.keyword_trends),
'temporal_data_points': sum(len(data) for data in self.temporal_data.values()),
'last_analysis': datetime.now().isoformat()
}
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