diff --git a/server/analysis/interactional.py b/server/analysis/interactional.py
index 8220747..53d97dc 100644
--- a/server/analysis/interactional.py
+++ b/server/analysis/interactional.py
@@ -1,9 +1,6 @@
 import pandas as pd
 import re
 
-from collections import Counter
-
-
 class InteractionAnalysis:
     def __init__(self, word_exclusions: set[str]):
         self.word_exclusions = word_exclusions
@@ -12,51 +9,6 @@ class InteractionAnalysis:
         tokens = re.findall(r"\b[a-z]{3,}\b", text)
         return [t for t in tokens if t not in self.word_exclusions]
 
-    def _vocab_richness_per_user(
-        self, df: pd.DataFrame, min_words: int = 20, top_most_used_words: int = 100
-    ) -> list:
-        df = df.copy()
-        df["content"] = df["content"].fillna("").astype(str).str.lower()
-        df["tokens"] = df["content"].apply(self._tokenize)
-
-        rows = []
-        for author, group in df.groupby("author"):
-            all_tokens = [t for tokens in group["tokens"] for t in tokens]
-
-            total_words = len(all_tokens)
-            unique_words = len(set(all_tokens))
-            events = len(group)
-
-            # Min amount of words for a user, any less than this might give weird results
-            if total_words < min_words:
-                continue
-
-            # 100% = they never reused a word (excluding stop words)
-            vocab_richness = unique_words / total_words
-            avg_words = total_words / max(events, 1)
-
-            counts = Counter(all_tokens)
-            top_words = [
-                {"word": w, "count": int(c)}
-                for w, c in counts.most_common(top_most_used_words)
-            ]
-
-            rows.append(
-                {
-                    "author": author,
-                    "events": int(events),
-                    "total_words": int(total_words),
-                    "unique_words": int(unique_words),
-                    "vocab_richness": round(vocab_richness, 3),
-                    "avg_words_per_event": round(avg_words, 2),
-                    "top_words": top_words,
-                }
-            )
-
-        rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
-
-        return rows
-
     def interaction_graph(self, df: pd.DataFrame):
         interactions = {a: {} for a in df["author"].dropna().unique()}
 
diff --git a/server/analysis/stat_gen.py b/server/analysis/stat_gen.py
index 8435340..c2f09ed 100644
--- a/server/analysis/stat_gen.py
+++ b/server/analysis/stat_gen.py
@@ -39,7 +39,7 @@ class StatGen:
         self.linguistic_analysis = LinguisticAnalysis(EXCLUDE_WORDS)
         self.cultural_analysis = CulturalAnalysis()
         self.summary_analysis = SummaryAnalysis()
-        self.user_analysis = UserAnalysis()
+        self.user_analysis = UserAnalysis(EXCLUDE_WORDS)
 
     ## Private Methods
     def _prepare_filtered_df(self, df: pd.DataFrame, filters: dict | None = None) -> pd.DataFrame:
diff --git a/server/analysis/user.py b/server/analysis/user.py
index d5e9917..fc8e618 100644
--- a/server/analysis/user.py
+++ b/server/analysis/user.py
@@ -1,7 +1,61 @@
 import pandas as pd
+import re
 
+from collections import Counter
 
 class UserAnalysis:
+    def __init__(self, word_exclusions: set[str]):
+        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 _vocab_richness_per_user(
+            self, df: pd.DataFrame, min_words: int = 20, top_most_used_words: int = 100
+        ) -> list:
+            df = df.copy()
+            df["content"] = df["content"].fillna("").astype(str).str.lower()
+            df["tokens"] = df["content"].apply(self._tokenize)
+
+            rows = []
+            for author, group in df.groupby("author"):
+                all_tokens = [t for tokens in group["tokens"] for t in tokens]
+
+                total_words = len(all_tokens)
+                unique_words = len(set(all_tokens))
+                events = len(group)
+
+                # Min amount of words for a user, any less than this might give weird results
+                if total_words < min_words:
+                    continue
+
+                # 100% = they never reused a word (excluding stop words)
+                vocab_richness = unique_words / total_words
+                avg_words = total_words / max(events, 1)
+
+                counts = Counter(all_tokens)
+                top_words = [
+                    {"word": w, "count": int(c)}
+                    for w, c in counts.most_common(top_most_used_words)
+                ]
+
+                rows.append(
+                    {
+                        "author": author,
+                        "events": int(events),
+                        "total_words": int(total_words),
+                        "unique_words": int(unique_words),
+                        "vocab_richness": round(vocab_richness, 3),
+                        "avg_words_per_event": round(avg_words, 2),
+                        "top_words": top_words,
+                    }
+                )
+
+            rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
+
+            return rows
+
     def top_users(self, df: pd.DataFrame) -> list:
         counts = df.groupby(["author", "source"]).size().sort_values(ascending=False)