updating sentiment classification

app.py

@@ -28,12 +28,12 @@ class SentimentResponse(BaseModel):
     sentiment_label: str
     confidence: float
 
-# Mapping BERT star ratings to sentiment scores
+# Updated mapping of BERT star ratings to sentiment scores
 LABEL_MAP = {
-    "1 star": -1.0,  # Very Negative
-    "2 stars": -0.5,  # Negative
+    "1 star": -1.0,   # Very Negative
+    "2 stars": -0.7,  # Negative
     "3 stars": 0.0,   # Neutral
-    "4 stars": 0.5,   # Positive
+    "4 stars": 0.7,   # Positive
     "5 stars": 1.0    # Very Positive
 }
 
@@ -49,10 +49,22 @@ def analyze_sentiment(request: SentimentRequest):
         sentiment_score = LABEL_MAP.get(label, 0.0)  # Default to neutral (0.0) if label is unexpected
         confidence = round(score, 3)
         
+        # Assign a sentiment label based on the score
+        if sentiment_score == -1.0:
+            sentiment_label = "very negative"
+        elif sentiment_score == -0.7:
+            sentiment_label = "negative"
+        elif sentiment_score == 0.0:
+            sentiment_label = "neutral"
+        elif sentiment_score == 0.7:
+            sentiment_label = "positive"
+        else:
+            sentiment_label = "very positive"
+        
         return SentimentResponse(
             content_id=request.content_id,
             sentiment_score=sentiment_score,
-            sentiment_label="positive" if sentiment_score > 0 else "neutral" if sentiment_score == 0 else "negative",
+            sentiment_label=sentiment_label,
             confidence=confidence
         )
     except Exception as e:
@@ -71,10 +83,22 @@ def analyze_sentiment_batch(request: BatchSentimentRequest):
             sentiment_score = LABEL_MAP.get(label, 0.0)
             confidence = round(score, 3)
             
+            # Assign a sentiment label based on the score
+            if sentiment_score == -1.0:
+                sentiment_label = "very negative"
+            elif sentiment_score == -0.7:
+                sentiment_label = "negative"
+            elif sentiment_score == 0.0:
+                sentiment_label = "neutral"
+            elif sentiment_score == 0.7:
+                sentiment_label = "positive"
+            else:
+                sentiment_label = "very positive"
+            
             responses.append(SentimentResponse(
                 content_id=post.content_id,
                 sentiment_score=sentiment_score,
-                sentiment_label="positive" if sentiment_score > 0 else "neutral" if sentiment_score == 0 else "negative",
+                sentiment_label=sentiment_label,
                 confidence=confidence
             ))
         return responses