feat(api): add cultural analysis endpoint with identity markers

server/analysis/cultural.py

@@ -0,0 +1,40 @@
+import pandas as pd
+import re
+
+class CulturalAnalysis:
+    def __init__(self, df: pd.DataFrame):
+        self.df = df
+
+    def get_identity_markers(self):
+        df = self.df.copy()
+        s = df["content"].fillna("").astype(str).str.lower()
+
+        in_group_words = {"we", "us", "our", "ourselves"}
+        out_group_words = {"they", "them", "their", "themselves"}
+
+        emotion_exclusions = {"emotion_neutral", "emotion_surprise"}
+        emotion_cols = [c for c in df.columns if c.startswith("emotion_") and c not in emotion_exclusions]
+
+        # token counts per row
+        tokens_per_row = s.apply(lambda txt: re.findall(r"\b[a-z]{2,}\b", txt))
+
+        total_tokens = int(tokens_per_row.map(len).sum())
+        in_hits = tokens_per_row.map(lambda toks: sum(t in in_group_words for t in toks))
+        out_hits = tokens_per_row.map(lambda toks: sum(t in out_group_words for t in toks))
+
+        in_count = int(in_hits.sum())
+        out_count = int(out_hits.sum())
+
+        result = {
+            "in_group_usage": in_count,
+            "out_group_usage": out_count,
+            "in_group_ratio": round(in_count / max(total_tokens, 1), 5),
+            "out_group_ratio": round(out_count / max(total_tokens, 1), 5),
+        }
+
+        if emotion_cols:
+            emo = df[emotion_cols].fillna(0).astype(float)
+            result["in_group_emotion_sums"] = emo[in_hits > out_hits].sum().to_dict()
+            result["out_group_emotion_sums"] = emo[out_hits > in_hits].sum().to_dict()
+
+        return result