| """
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| Sentiment Analysis Pydantic Models for MongoDB
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| Author: AI Generated
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| Created: 2025-11-24
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| Purpose: Define schemas for sentiment analysis results
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| """
|
|
|
| from pydantic import BaseModel, Field
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| from typing import List, Optional, Dict
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| from datetime import datetime
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| from bson import ObjectId
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|
|
|
|
| class PyObjectId(ObjectId):
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| """Custom ObjectId type for Pydantic v2"""
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|
|
| @classmethod
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| def __get_pydantic_core_schema__(cls, source_type, handler):
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| from pydantic_core import core_schema
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|
|
| return core_schema.union_schema([
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| core_schema.is_instance_schema(ObjectId),
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| core_schema.chain_schema([
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| core_schema.str_schema(),
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| core_schema.no_info_plain_validator_function(cls.validate),
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| ])
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| ],
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| serialization=core_schema.plain_serializer_function_ser_schema(
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| lambda x: str(x)
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| ))
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|
|
| @classmethod
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| def validate(cls, v):
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| if isinstance(v, ObjectId):
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| return v
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| if isinstance(v, str):
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| if not ObjectId.is_valid(v):
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| raise ValueError(f"Invalid ObjectId: {v}")
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| return ObjectId(v)
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| raise ValueError(f"Expected ObjectId or string, got {type(v)}")
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|
|
|
|
|
|
| class SentimentAnalysisResult(BaseModel):
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| """Individual sentiment analysis result for a comment/feedback"""
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| id: Optional[PyObjectId] = Field(default=None, alias="_id")
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| source_id: PyObjectId = Field(..., description="ID of the original comment/post")
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| source_type: str = Field(default="UserCommentPost", description="Type of source")
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|
|
|
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| event_code: str = Field(..., description="Event identifier this comment belongs to")
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|
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| sentiment_label: str = Field(..., description="Positive, Negative, or Neutral")
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| confidence_score: float = Field(..., ge=0.0, le=1.0, description="Model confidence (0-1)")
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|
|
| key_phrases: List[str] = Field(
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| default_factory=list,
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| description="Extracted keywords/phrases from the text"
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| )
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|
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| analyzed_at: datetime = Field(default_factory=datetime.utcnow)
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|
|
| class Config:
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| populate_by_name = True
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| arbitrary_types_allowed = True
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| json_encoders = {ObjectId: str}
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|
|
|
|
| class EventInsightReport(BaseModel):
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| """
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| High-level insights for an event, generated by LLM.
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| Includes Top 5 issues, NPS prediction, and improvement suggestions.
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| """
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| id: Optional[PyObjectId] = Field(default=None, alias="_id")
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| event_code: str = Field(..., description="Reference to EventVersion.EventCode")
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| report_date: datetime = Field(default_factory=datetime.utcnow)
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| total_comments: int = Field(0, description="Total number of comments analyzed")
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| sentiment_breakdown: Dict[str, int] = Field(
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| default_factory=dict,
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| description="Count by sentiment: { 'Positive': 50, 'Negative': 10, 'Neutral': 20 }"
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| )
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| predicted_nps: Optional[float] = Field(None, description="Predicted NPS score (0-100)")
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| top_issues: List[str] = Field(
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| default_factory=list,
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| description="Top 5 recurring issues, e.g., ['Check-in slow', 'Sound quality poor']"
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| )
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| improvement_suggestions: List[str] = Field(
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| default_factory=list,
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| description="AI-generated suggestions for improvement"
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| )
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|
|
| class Config:
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| populate_by_name = True
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| arbitrary_types_allowed = True
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| json_encoders = {ObjectId: str}
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|
|
