Fix arousal groupings per @dejanseo feedback: revised emotion categories, added threshold check for unclear axes

analyze_vectors.py

@@ -77,8 +77,12 @@ def valence_arousal_check(emotions, pca_results):
 
     positive = {"happy", "proud", "inspired", "loving", "hopeful", "calm", "playful"}
     negative = {"sad", "angry", "afraid", "desperate", "guilty", "disgusted", "lonely", "spiteful"}
-    high_arousal = {"angry", "afraid", "surprised", "desperate", "inspired", "nervous", "anxious"}
-    low_arousal = {"calm", "sad", "brooding", "lonely", "guilty"}
+    # Arousal groupings revised per dejanseo's feedback (2026-04-06):
+    # - Removed inspired from high arousal (more contemplative/medium)
+    # - Added disgusted, confused, playful, spiteful to high arousal
+    # - Added loving, hopeful to low arousal
+    high_arousal = {"angry", "afraid", "surprised", "desperate", "nervous", "anxious", "disgusted", "confused", "playful", "spiteful"}
+    low_arousal = {"calm", "sad", "brooding", "lonely", "guilty", "loving", "hopeful"}
 
     for pc_name, pc_vals in [("PC1", pca_results["pc1"]), ("PC2", pca_results["pc2"])]:
         pos_vals = [pc_vals[i] for i, e in enumerate(pca_results["emotions"]) if e in positive]
@@ -98,10 +102,12 @@ def valence_arousal_check(emotions, pca_results):
         print(f"    Positive mean: {pos_mean:+.3f}  Negative mean: {neg_mean:+.3f}  → Valence separation: {valence_sep:.3f}")
         print(f"    High-A mean:   {hi_mean:+.3f}  Low-A mean:    {lo_mean:+.3f}  → Arousal separation: {arousal_sep:.3f}")
 
-        if valence_sep > arousal_sep:
+        if valence_sep > arousal_sep and valence_sep > 1.0:
             print(f"    → {pc_name} ≈ VALENCE axis")
-        else:
+        elif arousal_sep > valence_sep and arousal_sep > 0.5:
             print(f"    → {pc_name} ≈ AROUSAL axis")
+        else:
+            print(f"    → {pc_name} ≈ UNCLEAR — neither valence nor arousal dominant (model may have learned its own geometry)")
 
 
 def main():