| def calculate_intent_score(raw_features: list) -> float: |
| """ |
| Transforms LLaMA qualitative INTENT/DECISION tags into quantitative score. |
| """ |
| intent_mapping = { |
| "strong buying intent": 1.0, |
| "ready to purchase": 1.0, |
| "high interest": 0.8, |
| "warm lead": 0.6, |
| "interested": 0.5, |
| "comparison shopper": 0.4, |
| "curious": 0.3, |
| "price sensitive": 0.3, |
| "hesitant buyer": 0.2, |
| "low interest": 0.1 |
| } |
| |
| decision_mapping = { |
| "converted": 1.0, |
| "ready to purchase": 1.0, |
| "near purchase": 0.9, |
| "negotiation": 0.8, |
| "evaluating": 0.6, |
| "comparing alternatives": 0.5, |
| "considering": 0.5, |
| "budget discussion": 0.4, |
| "exploring": 0.3, |
| "awareness": 0.2, |
| "follow-up required": 0.4, |
| "purchase delayed": 0.1, |
| "dropped": 0.0 |
| } |
| |
| intent_val = 0.5 |
| |
| for f in raw_features: |
| label = f.get("label", "") |
| name = str(f.get("value", f.get("name", ""))).lower() |
| |
| if label == "INTENT": |
| intent_val = max(intent_val, intent_mapping.get(name, 0.5)) |
| elif label == "DECISION_STAGE": |
| intent_val = max(intent_val, decision_mapping.get(name, 0.5)) |
| |
| return intent_val |
|
|
| def calculate_emotion_score(sentiment_score: float, raw_features: list) -> float: |
| """ |
| Combines VADER sentiment (-1 to 1) with LLaMA emotional tags to output a 0 to 1 score. |
| """ |
| |
| base_emotion = (sentiment_score + 1) / 2 |
| |
| emotion_val = base_emotion |
| for f in raw_features: |
| if f.get("label") == "EMOTIONAL_CONFIDENCE": |
| val = str(f.get("value", "")).lower() |
| if "high" in val or "strong" in val or "positive" in val: |
| emotion_val = min(1.0, emotion_val + 0.2) |
| elif "low" in val or "weak" in val or "negative" in val: |
| emotion_val = max(0.0, emotion_val - 0.2) |
| |
| return emotion_val |
|
|
