diff --git a/frontend/src/pages/Stats.tsx b/frontend/src/pages/Stats.tsx
index fc910c6..b20d01a 100644
--- a/frontend/src/pages/Stats.tsx
+++ b/frontend/src/pages/Stats.tsx
@@ -29,22 +29,24 @@ const StatPage = () => {
   useEffect(() => {
     Promise.all([
       axios.get("http://localhost:5000/stats/time"),
-      axios.get("http://localhost:5000/stats/word_frequencies"),
+      axios.get("http://localhost:5000/stats/content"),
     ])
       .then(([timeRes, wordsRes]) => {
+
         setPostsPerDay(timeRes.data["events_per_day"].filter(
           (d: any) => new Date(d.date) >= new Date('2026-01-10')
         ))
+
         setHeatmapData(timeRes.data["weekday_hour_heatmap"])
 
         setWordFrequencyData(
-          wordsRes.data.map((d: BackendWord) => ({
+          wordsRes.data["word_frequencies"].map((d: BackendWord) => ({
             text: d.word,
             value: d.count,
           }))
         );
       })
-      .catch(() => setError("Failed to load statistics"))
+      .catch((e) => setError("Failed to load statistics: " + e))
       .finally(() => setLoading(false));
   }, []);
 
@@ -64,7 +66,7 @@ const StatPage = () => {
         }}
       >
         <div>
-          <h2>Posts per Day</h2>
+          <h2>Events per Day</h2>
 
           <ResponsiveContainer width={800} height={350}>
             <LineChart data={postsPerDay}>
diff --git a/server/app.py b/server/app.py
index 4175aaa..0077a7c 100644
--- a/server/app.py
+++ b/server/app.py
@@ -42,16 +42,17 @@ def upload_data():
     
     return jsonify({"message": "File uploaded successfully", "event_count": len(stat_obj.df)}), 200
 
-@app.route('/stats/word_frequencies', methods=['GET'])
+@app.route('/stats/content', methods=['GET'])
 def word_frequencies():
     if stat_obj is None:
         return jsonify({"error": "No data uploaded"}), 400
     
     try:
-        return jsonify(stat_obj.get_word_frequencies().to_dict(orient='records')), 200
+        return jsonify(stat_obj.content_analysis()), 200
     except ValueError as e:
         return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
     except Exception as e:
+        print(traceback.format_exc())
         return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
     
 @app.route('/stats/search', methods=["POST"])
diff --git a/server/stat_gen.py b/server/stat_gen.py
index c33114f..686cd81 100644
--- a/server/stat_gen.py
+++ b/server/stat_gen.py
@@ -74,39 +74,7 @@ class StatGen:
             "burstiness": round(burst_index, 2)
         }
     
-    def get_word_frequencies(self, limit: int = 100) -> pd.DataFrame:
-        texts = (
-            self.df["content"]
-            .dropna()
-            .astype(str)
-            .str.lower()
-        )
-
-        words = []
-        for text in texts:
-            tokens = re.findall(r"\b[a-z]{3,}\b", text)
-            words.extend(
-                w for w in tokens
-                if w not in EXCLUDE_WORDS
-            )
-
-        counts = Counter(words)
-
-        return (
-            pd.DataFrame(counts.items(), columns=["word", "count"])
-            .sort_values("count", ascending=False)
-            .head(limit)
-            .reset_index(drop=True)
-        )
-    
-    def filter_events(self, search_query: str) -> pd.DataFrame:
-        self.df = self.df[self.df["content"].str.contains(search_query)]
-        return self.df
-    
-    def reset_dataset(self) -> None:
-        self.df = self.original_df.copy(deep=True)
-
-    def get_summary(self) -> dict:
+    def summary(self) -> dict:
         total_posts = (self.df["type"] == "post").sum()
         total_comments = (self.df["type"] == "comment").sum()
 
@@ -126,4 +94,39 @@ class StatGen:
             "sources": self.df["source"].unique().tolist()
         }
 
+    def content_analysis(self, limit: int = 100) -> dict:
+        texts = (
+            self.df["content"]
+            .dropna()
+            .astype(str)
+            .str.lower()
+        )
+
+        words = []
+        for text in texts:
+            tokens = re.findall(r"\b[a-z]{3,}\b", text)
+            words.extend(
+                w for w in tokens
+                if w not in EXCLUDE_WORDS
+            )
+
+        counts = Counter(words)
+
+        word_frequencies = (
+            pd.DataFrame(counts.items(), columns=["word", "count"])
+            .sort_values("count", ascending=False)
+            .head(limit)
+            .reset_index(drop=True)
+        )
+
+        return {
+            "word_frequencies": word_frequencies.to_dict(orient='records')
+        }
+    
+    def filter_events(self, search_query: str) -> pd.DataFrame:
+        self.df = self.df[self.df["content"].str.contains(search_query)]
+        return self.df
+    
+    def reset_dataset(self) -> None:
+        self.df = self.original_df.copy(deep=True)