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ef78361 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 | """Sentiment API methods for ChainShiftClient.
Mixin class extracted from api_client.py.
Methods access self._get, self._post, self._session, self.base_url, self.headers from the parent class.
"""
from typing import Any
class SentimentApiMixin:
"""Gen3 Nudge, Brand Mentions, LLM Verification, Feedback, Keyword APIs."""
# ========================================================================
# Gen3 APIs - Nudge Detection
# ========================================================================
def get_nudge_candidates(
self,
campaign_id: int,
page: int = 1,
page_size: int = 50,
bit_quadrant: str | None = None,
platform: str | None = None,
) -> dict:
"""Get answers flagged for nudge (in-house brand negative mentions).
Returns:
- total_nudge_candidates: int
- by_confidence_tier: {HIGH: n, MEDIUM: n, LOW: n}
- by_platform: {CHATGPT: n, GOOGLE_AI: n, ...}
- by_cej: {AWARENESS_COMPARISON: n, PURCHASE: n, ...}
- by_bit_quadrant: {neutral: n, ...}
- candidates: list of nudge items
"""
params = {"page": page, "page_size": page_size}
if bit_quadrant:
params["bit_quadrant"] = bit_quadrant
if platform:
params["platform"] = platform
return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/nudge-candidates", params)
def get_brand_mentions(
self,
campaign_id: int,
page: int = 1,
page_size: int = 50,
brand_type: str | None = None,
polarity: str | None = None,
llm_verified: str | None = None,
brand_name: str | None = None,
sort_by: str | None = "analyzed_at",
sort_desc: bool = True,
) -> dict:
"""Get brand mention analysis."""
params = {"page": page, "page_size": page_size, "sort_desc": sort_desc}
if brand_type:
params["brand_type"] = brand_type
if polarity:
params["polarity"] = polarity
if llm_verified:
params["llm_verified"] = llm_verified
if brand_name:
params["brand_name"] = brand_name
if sort_by:
params["sort_by"] = sort_by
return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/brand-mentions", params)
def export_brand_mentions(
self,
campaign_id: int,
brand_type: str | None = None,
polarity: str | None = None,
llm_verified: str | None = None,
brand_name: str | None = None,
include_full_answers: bool = False,
) -> bytes:
"""Export brand mentions to Excel."""
params = {}
if brand_type:
params["brand_type"] = brand_type
if polarity:
params["polarity"] = polarity
if llm_verified:
params["llm_verified"] = llm_verified
if brand_name:
params["brand_name"] = brand_name
if include_full_answers:
params["include_full_answers"] = "true"
url = f"{self.base_url}/api/v1/sentiment/campaigns/{campaign_id}/brand-mentions/export"
response = self._session.get(url, params=params, headers=self.headers, timeout=120)
response.raise_for_status()
return response.content
# ========================================================================
# LLM Verification & Human Feedback APIs
# ========================================================================
def verify_answer(self, answer_id: int, force: bool = False) -> dict:
"""Request LLM 2차 검증 for an answer."""
return self._post("/api/v1/sentiment/verify", {
"answer_id": answer_id,
"force": force,
})
def submit_feedback(
self,
answer_id: int,
campaign_id: int,
feedback_type: str,
corrected_polarity: str | None = None,
wrong_reason: str | None = None,
comment: str | None = None,
) -> dict:
"""Submit human feedback for an answer sentiment."""
data = {
"answer_id": answer_id,
"campaign_id": campaign_id,
"feedback_type": feedback_type,
}
if corrected_polarity:
data["corrected_polarity"] = corrected_polarity
if wrong_reason:
data["wrong_reason"] = wrong_reason
if comment:
data["comment"] = comment
return self._post("/api/v1/sentiment/feedback", data)
def get_feedback_stats(self, campaign_id: int) -> dict:
"""Get feedback statistics for a campaign."""
return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/feedback/stats")
def get_full_answers(self, answer_ids: list[int]) -> dict:
"""Get full answer text for multiple answers."""
return self._post("/api/v1/sentiment/answers/full", {"answer_ids": answer_ids})
def export_nudge_candidates(
self,
campaign_id: int,
include_full_answers: bool = False,
include_evidence: bool = True,
llm_verified_only: bool = False,
platform: str | None = None,
llm_is_negative: bool | None = None,
) -> bytes:
"""Export nudge candidates to Excel."""
url = f"{self.base_url}/api/v1/sentiment/campaigns/{campaign_id}/nudge-candidates/export"
params = {
"include_full_answers": str(include_full_answers).lower(),
"include_evidence": str(include_evidence).lower(),
"llm_verified_only": str(llm_verified_only).lower(),
}
if platform:
params["platform"] = platform
if llm_is_negative is not None:
params["llm_is_negative"] = str(llm_is_negative).lower()
response = self._session.get(url, headers=self.headers, params=params, timeout=120)
response.raise_for_status()
return response.content
# ========================================================================
# Keyword Sentiment APIs
# ========================================================================
def start_keyword_analysis(self, campaign_id: int, keywords: list[str]) -> dict:
"""Start a keyword sentiment analysis job."""
return self._post(
f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-analysis",
{"keywords": keywords},
)
def get_keyword_summary(
self,
campaign_id: int,
keyword: str | None = None,
page: int = 1,
page_size: int = 50,
) -> dict:
"""Get keyword sentiment summary with optional filter and pagination."""
params: dict[str, Any] = {"page": page, "page_size": page_size}
if keyword:
params["keyword"] = keyword
return self._get(f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-summary", params)
def get_keyword_results(
self,
campaign_id: int,
keyword: str | None = None,
sentiment: str | None = None,
brand_name: str | None = None,
competitor: bool = False,
llm_verified: str | None = None,
llm_is_negative: bool | None = None,
platform: str | None = None,
brand_only: bool = False,
no_brand: bool = False,
page: int = 1,
page_size: int = 50,
) -> dict:
"""Get keyword results with filters."""
params: dict[str, Any] = {"page": page, "page_size": page_size}
if keyword:
params["keyword"] = keyword
if sentiment:
params["sentiment"] = sentiment
if brand_name:
params["brand_name"] = brand_name
if competitor:
params["competitor"] = "true"
if llm_verified:
params["llm_verified"] = llm_verified
if llm_is_negative is not None:
params["llm_is_negative"] = str(llm_is_negative).lower()
if platform:
params["platform"] = platform
if brand_only:
params["brand_only"] = "true"
if no_brand:
params["no_brand"] = "true"
return self._get(
f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-results", params
)
def get_keyword_brand_analysis(
self,
campaign_id: int,
keyword: str | None = None,
brand_name: str | None = None,
competitor: bool = False,
) -> dict:
"""Get keyword x brand cross analysis."""
params: dict[str, Any] = {}
if keyword:
params["keyword"] = keyword
if brand_name:
params["brand_name"] = brand_name
if competitor:
params["competitor"] = "true"
return self._get(
f"/api/v1/sentiment/campaigns/{campaign_id}/keyword-brand-analysis", params
)
def export_keyword_results(
self,
campaign_id: int,
keyword: str | None = None,
sentiment: str | None = None,
brand_name: str | None = None,
competitor: bool = False,
llm_verified: str | None = None,
llm_is_negative: bool | None = None,
platform: str | None = None,
brand_only: bool = False,
no_brand: bool = False,
format: str = "xlsx",
) -> bytes:
"""Export keyword results to Excel or CSV with same filters as list endpoint."""
params: dict[str, Any] = {"format": format}
if keyword:
params["keyword"] = keyword
if sentiment:
params["sentiment"] = sentiment
if brand_name:
params["brand_name"] = brand_name
if competitor:
params["competitor"] = "true"
if llm_verified:
params["llm_verified"] = llm_verified
if llm_is_negative is not None:
params["llm_is_negative"] = str(llm_is_negative).lower()
if platform:
params["platform"] = platform
if brand_only:
params["brand_only"] = "true"
if no_brand:
params["no_brand"] = "true"
url = f"{self.base_url}/api/v1/sentiment/campaigns/{campaign_id}/keyword-results/export"
response = self._session.get(url, params=params, headers=self.headers, timeout=120)
response.raise_for_status()
return response.content
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