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Metrics calculation for sentiment analysis dashboard
Provides key performance indicators and statistical metrics
"""
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple
class SentimentMetrics:
"""
Calculates various metrics for sentiment analysis
"""
@staticmethod
def calculate_overall_metrics(df):
"""
Calculate overall summary metrics
Args:
df: Sentiment dataframe
Returns:
dict: Overall metrics
"""
total_comments = len(df)
total_reply_required = df['requires_reply'].sum() if 'requires_reply' in df.columns else 0
# Sentiment distribution
sentiment_dist = df['sentiment_polarity'].value_counts(normalize=True) * 100
# Calculate sentiment score (vectorized — no copy needed)
sentiment_weights = {
'very_negative': -2,
'negative': -1,
'neutral': 0,
'positive': 1,
'very_positive': 2
}
avg_sentiment_score = df['sentiment_polarity'].map(sentiment_weights).mean()
# Negative sentiment percentage
negative_sentiments = ['negative', 'very_negative']
negative_pct = (df['sentiment_polarity'].isin(negative_sentiments).sum() / total_comments * 100) if total_comments > 0 else 0
# Positive sentiment percentage
positive_sentiments = ['positive', 'very_positive']
positive_pct = (df['sentiment_polarity'].isin(positive_sentiments).sum() / total_comments * 100) if total_comments > 0 else 0
return {
'total_comments': total_comments,
'total_reply_required': int(total_reply_required),
'reply_required_pct': (total_reply_required / total_comments * 100) if total_comments > 0 else 0,
'avg_sentiment_score': avg_sentiment_score,
'negative_pct': negative_pct,
'positive_pct': positive_pct,
'sentiment_distribution': sentiment_dist.to_dict()
}
@staticmethod
def calculate_brand_metrics(df):
"""
Calculate metrics by brand
Args:
df: Sentiment dataframe
Returns:
dict: Metrics by brand
"""
brand_metrics = {}
for brand in df['brand'].unique():
brand_df = df[df['brand'] == brand]
brand_metrics[brand] = SentimentMetrics.calculate_overall_metrics(brand_df)
return brand_metrics
@staticmethod
def calculate_platform_metrics(df):
"""
Calculate metrics by platform
Args:
df: Sentiment dataframe
Returns:
dict: Metrics by platform
"""
platform_metrics = {}
for platform in df['platform'].unique():
platform_df = df[df['platform'] == platform]
platform_metrics[platform] = SentimentMetrics.calculate_overall_metrics(platform_df)
return platform_metrics
@staticmethod
def calculate_content_engagement_score(content_df):
"""
Calculate engagement score for a content piece
Args:
content_df: DataFrame for a single content
Returns:
float: Engagement score (0-100)
"""
if len(content_df) == 0:
return 0
# Factors:
# 1. Number of comments (normalized)
# 2. Sentiment positivity
# 3. Intent diversity
# 4. Reply requirement rate
comment_count = len(content_df)
comment_score = min(comment_count / 100 * 30, 30) # Max 30 points for 100+ comments
# Sentiment score (max 40 points) — vectorized, no copy needed
sentiment_weights = {
'very_negative': -2,
'negative': -1,
'neutral': 0,
'positive': 1,
'very_positive': 2
}
avg_sentiment = content_df['sentiment_polarity'].map(sentiment_weights).mean()
sentiment_score = ((avg_sentiment + 2) / 4) * 40 # Normalize to 0-40
# Intent diversity score (max 20 points)
unique_intents = content_df['intent'].str.split(',').explode().str.strip().nunique()
intent_score = min(unique_intents / 8 * 20, 20) # Max 20 points for 8 unique intents
# Interaction requirement (max 10 points)
reply_rate = content_df['requires_reply'].sum() / len(content_df) if len(content_df) > 0 else 0
interaction_score = reply_rate * 10
total_score = comment_score + sentiment_score + intent_score + interaction_score
return round(total_score, 2)
@staticmethod
def get_sentiment_health_status(negative_pct):
"""
Determine health status based on negative sentiment percentage
Args:
negative_pct: Percentage of negative sentiments
Returns:
tuple: (status, color)
"""
if negative_pct < 10:
return ("Excellent", "green")
elif negative_pct < 20:
return ("Good", "lightgreen")
elif negative_pct < 30:
return ("Fair", "orange")
elif negative_pct < 50:
return ("Poor", "darkorange")
else:
return ("Critical", "red")
@staticmethod
def calculate_intent_priority_score(intent_counts):
"""
Calculate priority score for different intents
Args:
intent_counts: Dictionary of intent counts
Returns:
dict: Priority scores for each intent
"""
# Priority weights (higher = more urgent)
priority_weights = {
'feedback_negative': 5,
'request': 4,
'question': 4,
'suggestion': 3,
'praise': 2,
'humor_sarcasm': 1,
'off_topic': 1,
'spam_selfpromo': 0
}
priority_scores = {}
for intent, count in intent_counts.items():
weight = priority_weights.get(intent, 1)
priority_scores[intent] = count * weight
return priority_scores
@staticmethod
def calculate_response_urgency(df):
"""
Calculate response urgency metrics
Args:
df: Sentiment dataframe
Returns:
dict: Urgency metrics
"""
reply_required_df = df[df['requires_reply'] == True]
if len(reply_required_df) == 0:
return {
'urgent_count': 0,
'high_priority_count': 0,
'medium_priority_count': 0,
'low_priority_count': 0
}
# Classify urgency based on sentiment and intent
urgent = reply_required_df[
reply_required_df['sentiment_polarity'].isin(['very_negative', 'negative'])
]
high_priority = reply_required_df[
(reply_required_df['sentiment_polarity'] == 'neutral') &
(reply_required_df['intent'].str.contains('feedback_negative|request', na=False))
]
medium_priority = reply_required_df[
reply_required_df['sentiment_polarity'] == 'positive'
]
low_priority = reply_required_df[
reply_required_df['sentiment_polarity'] == 'very_positive'
]
return {
'urgent_count': len(urgent),
'high_priority_count': len(high_priority),
'medium_priority_count': len(medium_priority),
'low_priority_count': len(low_priority)
}
@staticmethod
def calculate_trend_indicator(df, current_period, previous_period, metric='sentiment_score'):
"""
Calculate trend indicator comparing two periods
Args:
df: Sentiment dataframe
current_period: Tuple of (start_date, end_date) for current period
previous_period: Tuple of (start_date, end_date) for previous period
metric: Metric to compare
Returns:
dict: Trend information
"""
if 'comment_timestamp' not in df.columns:
return {'trend': 'stable', 'change': 0}
# Filter data for each period
current_df = df[
(df['comment_timestamp'] >= pd.Timestamp(current_period[0])) &
(df['comment_timestamp'] <= pd.Timestamp(current_period[1]))
]
previous_df = df[
(df['comment_timestamp'] >= pd.Timestamp(previous_period[0])) &
(df['comment_timestamp'] <= pd.Timestamp(previous_period[1]))
]
if len(current_df) == 0 or len(previous_df) == 0:
return {'trend': 'stable', 'change': 0}
# Calculate metric for each period
if metric == 'sentiment_score':
# Vectorized — no copy needed
sentiment_weights = {
'very_negative': -2, 'negative': -1, 'neutral': 0,
'positive': 1, 'very_positive': 2
}
current_value = current_df['sentiment_polarity'].map(sentiment_weights).mean()
previous_value = previous_df['sentiment_polarity'].map(sentiment_weights).mean()
else:
current_value = len(current_df)
previous_value = len(previous_df)
# Calculate change
change = ((current_value - previous_value) / previous_value * 100) if previous_value != 0 else 0
# Determine trend
if abs(change) < 5:
trend = 'stable'
elif change > 0:
trend = 'improving' if metric == 'sentiment_score' else 'increasing'
else:
trend = 'declining' if metric == 'sentiment_score' else 'decreasing'
return {
'trend': trend,
'change': round(change, 2),
'current_value': round(current_value, 2),
'previous_value': round(previous_value, 2)
} |