Upload sentiment_analyzer.py

sentiment_analyzer.py

@@ -180,13 +180,15 @@ class LexiconBasedAnalyzer:
                             negation_count += 1
                             break
             
-            # Check for intensifiers
-            if i > 0 and tokens[i-1] in self.lexicon.intensifiers:
-                intensifier_strength = self.lexicon.intensifiers[tokens[i-1]]
+            # Check for intensifiers (look back up to 2 tokens)
+            for k in range(max(0, i-2), i):
+                if k >= 0 and tokens[k] in self.lexicon.intensifiers:
+                    intensifier_strength = max(intensifier_strength, self.lexicon.intensifiers[tokens[k]])
             
-            # Check for diminishers
-            if i > 0 and tokens[i-1] in self.lexicon.diminishers:
-                diminisher_strength = self.lexicon.diminishers[tokens[i-1]]
+            # Check for diminishers (look back up to 2 tokens)
+            for k in range(max(0, i-2), i):
+                if k >= 0 and tokens[k] in self.lexicon.diminishers:
+                    diminisher_strength = min(diminisher_strength, self.lexicon.diminishers[tokens[k]])
             
             # Check sentiment
             if token in self.lexicon.positive_words:
@@ -208,17 +210,26 @@ class LexiconBasedAnalyzer:
             
             i += 1
         
-        # Calculate final sentiment
-        total_score = positive_score + negative_score
-        if total_score == 0:
+        # Calculate final sentiment with improved scoring
+        # Normalize scores to prevent extreme values from dominating
+        total_raw = positive_score + negative_score
+        if total_raw > 0:
+            # Use logarithmic scaling for better balance (but keep original for display)
+            pos_normalized = positive_score / total_raw
+            neg_normalized = negative_score / total_raw
+        else:
+            pos_normalized = 0.0
+            neg_normalized = 0.0
+        
+        if total_raw == 0:
             polarity = 'neutral'
             confidence = 0.0
         elif positive_score > negative_score:
             polarity = 'positive'
-            confidence = positive_score / total_score if total_score > 0 else 0.0
+            confidence = pos_normalized
         else:
             polarity = 'negative'
-            confidence = negative_score / total_score if total_score > 0 else 0.0
+            confidence = neg_normalized
         
         return {
             'polarity': polarity,