diff --git a/server/stat_gen.py b/server/stat_gen.py
index e781580..980990e 100644
--- a/server/stat_gen.py
+++ b/server/stat_gen.py
@@ -2,11 +2,19 @@ import pandas as pd
 import re
 import nltk
 import datetime
+import torch
 
 from nltk.corpus import stopwords
 from collections import Counter
+from transformers import pipeline
 
-from pprint import pprint
+emotion_classifier = pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    top_k=None,
+    truncation=True,
+    device=0 if torch.cuda.is_available() else -1
+)
 
 DOMAIN_STOPWORDS = {
     "www", "https", "http",
@@ -30,17 +38,37 @@ class StatGen:
         comments_df["parent_id"] = comments_df.get("post_id")
 
         self.df = pd.concat([posts_df, comments_df])
-        self._add_date_cols(self.df)
+        self._add_extra_cols(self.df)
+        self._add_emotion_cols(self.df)
 
         self.original_df = self.df.copy(deep=True)
 
     ## Private Methods
-    def _add_date_cols(self, df: pd.DataFrame) -> None:
+    def _add_extra_cols(self, df: pd.DataFrame) -> None:
         df['date'] = pd.to_datetime(df['timestamp'], unit='s').dt.date
         df["dt"] = pd.to_datetime(df["timestamp"], unit="s", utc=True)
         df["hour"] = df["dt"].dt.hour
         df["weekday"] = df["dt"].dt.day_name()
 
+    def _add_emotion_cols(self, df: pd.DataFrame) -> None:
+        texts = df["content"].astype(str).str.slice(0, 512).tolist()
+
+        results = emotion_classifier(
+            texts,
+            batch_size=64
+        )
+
+        labels = [r["label"] for r in results[0]]
+
+        for label in labels:
+            df[f"emotion_{label}"] = [
+                next(item["score"] for item in row if item["label"] == label)
+                for row in results
+            ]
+
+        # strongest emotion per row (much more meaningful than sums)
+        df["emotion_intensity"] = df.filter(like="emotion_").max(axis=1)
+
     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 EXCLUDE_WORDS]