feat(api): add cultural endpoint

server/analysis/linguistic.py

@@ -9,6 +9,10 @@ class LinguisticAnalysis:
         self.df = df
         self.word_exclusions = word_exclusions
 
+    def _tokenize(self, text: str):
+        tokens = re.findall(r"\b[a-z]{3,}\b", text)
+        return [t for t in tokens if t not in self.word_exclusions]
+
     def _clean_text(self, text: str) -> str:
         text = re.sub(r"http\S+", "", text)        # remove URLs
         text = re.sub(r"www\S+", "", text)
@@ -65,4 +69,45 @@ class LinguisticAnalysis:
             .sort_values("count", ascending=False)
             .head(limit)
             .to_dict(orient="records")
-        )
+        )
+        
+    def identity_markers(self):
+        df = self.df.copy()
+        df["content"] = 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 = [
+            col for col in self.df.columns
+            if col.startswith("emotion_") and col not in emotion_exclusions
+        ]
+        in_count = 0
+        out_count = 0
+        in_emotions = {e: 0 for e in emotion_cols}
+        out_emotions = {e: 0 for e in emotion_cols}
+        total = 0
+
+        for post in df:
+            text = post["content"]
+            tokens = re.findall(r"\b[a-z]{2,}\b", text)
+            total += len(tokens)
+            in_count += sum(t in in_group_words for t in tokens)
+            out_count += sum(t in out_group_words for t in tokens)
+
+            emotions = post[emotion_cols]
+            print(emotions)
+
+            
+
+        return {
+            "in_group_usage": in_count,
+            "out_group_usage": out_count,
+            "in_group_ratio": round(in_count / max(total, 1), 5),
+            "out_group_ratio": round(out_count / max(total, 1), 5),
+        }