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"""Thread length and sentiment correlation analysis."""
import logging
from typing import List, Dict, Optional, Tuple
import pandas as pd
import numpy as np
from scipy import stats
from sklearn.linear_model import LinearRegression
from analysis.sentiment_analyzer import SentimentAnalyzer
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ThreadAnalyzer:
"""
Analyze correlation between thread length and sentiment.
Thread length is the number of comments under a news article.
Temperature is the probability that a comment is negative.
"""
def __init__(self, sentiment_analyzer: Optional[SentimentAnalyzer] = None):
"""
Initialize thread analyzer.
Args:
sentiment_analyzer: SentimentAnalyzer instance (creates new if None)
"""
if sentiment_analyzer is None:
self.analyzer = SentimentAnalyzer()
else:
self.analyzer = sentiment_analyzer
def calculate_thread_lengths(
self,
data: List[Dict],
news_id_key: str = "news_id",
comment_id_key: str = "id"
) -> Dict[str, int]:
"""
Calculate thread length (number of comments) for each news item.
Args:
data: List of comment dictionaries
news_id_key: Key for news ID in data dict
comment_id_key: Key for comment ID in data dict
Returns:
Dictionary mapping news_id to thread length
Example:
>>> analyzer = ThreadAnalyzer()
>>> data = [
... {"news_id": 1, "id": 1},
... {"news_id": 1, "id": 2},
... {"news_id": 2, "id": 3},
... ]
>>> lengths = analyzer.calculate_thread_lengths(data)
>>> lengths[1]
2
"""
thread_lengths = {}
for item in data:
news_id = item.get(news_id_key)
if news_id:
thread_lengths[news_id] = thread_lengths.get(news_id, 0) + 1
return thread_lengths
def calculate_temperature(
self,
data: List[Dict],
news_id_key: str = "news_id",
text_key: str = "text"
) -> Dict[str, float]:
"""
Calculate temperature (negative sentiment probability) for each news item.
Temperature is the probability that a comment is negative.
Args:
data: List of comment dictionaries
news_id_key: Key for news ID in data dict
text_key: Key for text in data dict
Returns:
Dictionary mapping news_id to average temperature
"""
# Group comments by news_id
news_comments = {}
for item in data:
news_id = item.get(news_id_key)
text = item.get(text_key)
if news_id and text:
if news_id not in news_comments:
news_comments[news_id] = []
news_comments[news_id].append(text)
# Calculate temperature for each news item
temperatures = {}
for news_id, texts in news_comments.items():
# Analyze sentiment
results = self.analyzer.analyze_batch(texts)
# Calculate average temperature (probability of negative)
negative_scores = []
for result in results:
label = result["label"]
score = result["score"]
if label == "NEGATIVE":
# High confidence negative = high temperature
negative_scores.append(score)
elif label == "POSITIVE":
# High confidence positive = low temperature
negative_scores.append(1.0 - score)
else:
# Neutral = medium temperature
negative_scores.append(0.5)
avg_temperature = np.mean(negative_scores) if negative_scores else 0.5
temperatures[news_id] = avg_temperature
return temperatures
def analyze_correlation(
self,
thread_lengths: Dict[str, int],
temperatures: Dict[str, float]
) -> Dict:
"""
Analyze correlation between thread length and temperature.
Args:
thread_lengths: Dictionary mapping news_id to thread length
temperatures: Dictionary mapping news_id to temperature
Returns:
Dictionary with correlation statistics
"""
# Get common news_ids
common_ids = set(thread_lengths.keys()) & set(temperatures.keys())
if len(common_ids) < 2:
return {
"correlation": 0.0,
"p_value": 1.0,
"significant": False,
"sample_size": len(common_ids),
"error": "Insufficient data for correlation analysis"
}
# Prepare data
lengths = [thread_lengths[id] for id in common_ids]
temps = [temperatures[id] for id in common_ids]
# Calculate Pearson correlation
correlation, p_value = stats.pearsonr(lengths, temps)
# Linear regression
X = np.array(lengths).reshape(-1, 1)
y = np.array(temps)
reg = LinearRegression()
reg.fit(X, y)
slope = reg.coef_[0]
intercept = reg.intercept_
r_squared = reg.score(X, y)
return {
"correlation": float(correlation),
"p_value": float(p_value),
"significant": p_value < 0.05,
"sample_size": len(common_ids),
"slope": float(slope),
"intercept": float(intercept),
"r_squared": float(r_squared),
"interpretation": self._interpret_correlation(correlation, p_value)
}
def _interpret_correlation(self, correlation: float, p_value: float) -> str:
"""Interpret correlation results."""
if p_value >= 0.05:
return "No significant correlation (p >= 0.05)"
if abs(correlation) < 0.1:
strength = "negligible"
elif abs(correlation) < 0.3:
strength = "weak"
elif abs(correlation) < 0.5:
strength = "moderate"
elif abs(correlation) < 0.7:
strength = "strong"
else:
strength = "very strong"
direction = "positive" if correlation > 0 else "negative"
return f"{strength.capitalize()} {direction} correlation (r={correlation:.3f}, p={p_value:.4f})"
def analyze_from_dataframe(
self,
df: pd.DataFrame,
news_id_col: str = "news_id",
text_col: str = "text"
) -> Tuple[pd.DataFrame, Dict]:
"""
Analyze thread-sentiment correlation from DataFrame.
Args:
df: DataFrame with news_id and text columns
news_id_col: Name of news_id column
text_col: Name of text column
Returns:
Tuple of (DataFrame with thread stats, correlation results)
"""
# Convert to list of dicts
data = df[[news_id_col, text_col]].to_dict('records')
# Calculate thread lengths and temperatures
thread_lengths = self.calculate_thread_lengths(
data,
news_id_key=news_id_col
)
temperatures = self.calculate_temperature(
data,
news_id_key=news_id_col,
text_key=text_col
)
# Analyze correlation
correlation_results = self.analyze_correlation(thread_lengths, temperatures)
# Create DataFrame with thread statistics
common_ids = set(thread_lengths.keys()) & set(temperatures.keys())
thread_stats = pd.DataFrame([
{
"news_id": news_id,
"thread_length": thread_lengths[news_id],
"temperature": temperatures[news_id]
}
for news_id in common_ids
])
return thread_stats, correlation_results
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