Skip to content

Temporal Sentiment

Track how sentiment changes across time periods.

Usage

reviews_over_time = [
    {"text": "Great product!", "date": "2024-01-01"},
    {"text": "Quality seems worse", "date": "2024-03-01"},
    {"text": "Terrible now", "date": "2024-06-01"},
]

timeline = await sm.analyze_temporal(reviews_over_time)

for period in timeline:
    print(f"{period.date}: {period.sentiment} ({period.avg_score:.2f})")

Output:

2024-01: positive (0.85)
2024-03: negative (-0.42)
2024-06: negative (-0.89)

Use Cases

  • Product quality tracking
  • Brand perception monitoring
  • Campaign impact analysis
  • Seasonal trends

Aggregation Periods

# Monthly aggregation
timeline = await sm.analyze_temporal(reviews, period="month")

# Weekly aggregation
timeline = await sm.analyze_temporal(reviews, period="week")

# Daily aggregation
timeline = await sm.analyze_temporal(reviews, period="day")

# Identify sentiment shifts
for i, period in enumerate(timeline[1:], 1):
    prev = timeline[i-1]
    change = period.avg_score - prev.avg_score

    if change < -0.3:
        print(f"Significant drop at {period.date}")
    elif change > 0.3:
        print(f"Significant improvement at {period.date}")