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