Update analyzer.py

analyzer.py

@@ -303,7 +303,7 @@ def _load_svm(filename: str, stream_name: str) -> bool:
     #  Validate 
     if hasattr(obj, "predict") or hasattr(obj, "decision_function") or hasattr(obj, "predict_proba"):
         _svm_pipelines[stream_name] = obj
-        logger.info("✅ SVM loaded: %s → %s", stream_name, type(obj).__name__)
+        logger.info(" SVM loaded: %s → %s", stream_name, type(obj).__name__)
         return True
 
     logger.warning("Object for %s has no sklearn API — type=%s", stream_name, type(obj).__name__)
@@ -666,7 +666,7 @@ def detect_misinformation(
             + (f" ({_load_error})" if _load_error else "")
         )
 
-    label = " Potential Misinformation" if score >= 0.5 else "✅ Appears Credible"
+    label = " Potential Misinformation" if score >= 0.5 else "Appears Credible"
 
     # Strip internal 'source' key from modality dicts (not expected by charts)
     clean_modality = {
@@ -733,7 +733,7 @@ def analyze_sentiment_batch(
             vs = vader.polarity_scores(text)
             c  = vs["compound"]
             results.append({
-                "label":    "POSITIVE" if c >= 0.05 else ("NEGATIVE" if c <= -0.05 else "NEUTRAL"),
+                "label":    "POSITIVELY ENGAGEMENT" if c >= 0.05 else ("NEGATIVELY ENGAGEMENT" if c <= -0.05 else "NEUTRAL"),
                 "score":    abs(c),
                 "compound": c,
             })
@@ -762,8 +762,8 @@ def _simple_lexicon_sentiment(texts: List[str]) -> List[Dict]:
     for text in texts:
         words = set(text.lower().split())
         p = len(words & pos); n = len(words & neg)
-        if   p > n: out.append({"label": "POSITIVE", "score": 0.7, "compound":  0.5})
-        elif n > p: out.append({"label": "NEGATIVE", "score": 0.7, "compound": -0.5})
+        if   p > n: out.append({"label": "POSITIVELY ENGAGEMENT", "score": 0.7, "compound":  0.5})
+        elif n > p: out.append({"label": "NEGATIVE ENGAGEMENT", "score": 0.7, "compound": -0.5})
         else:       out.append({"label": "NEUTRAL",  "score": 0.5, "compound":  0.0})
     return out
 
@@ -776,8 +776,8 @@ def sentiment_summary(results: List[Dict]) -> Dict:
     total  = len(results)
     avg    = float(np.mean([r.get("compound", 0.0) for r in results]))
     return {
-        "POSITIVE":     counts.get("POSITIVE", 0),
-        "NEGATIVE":     counts.get("NEGATIVE", 0),
+        "POSITIVELY ENGAGEMENT":     counts.get("POSITIVE", 0),
+        "NEGATIVELY ENGAGEMENT":     counts.get("NEGATIVE", 0),
         "NEUTRAL":      counts.get("NEUTRAL",  0),
         "total":        total,
         "avg_compound": round(avg, 3),
@@ -822,6 +822,6 @@ def sentiment_weighted_keywords(
     for text, sent in zip(texts, sentiment_results):
         tokens = [t for t in re.findall(r"[a-zA-Z]{3,}", text.lower()) if t not in STOPWORDS]
         weight = sent.get("score", 0.5)
-        if   sent["label"] == "POSITIVE": pos_freq.update({t: weight for t in tokens})
-        elif sent["label"] == "NEGATIVE": neg_freq.update({t: weight for t in tokens})
+        if   sent["label"] == "POSITIVELY ENGAGEMENT": pos_freq.update({t: weight for t in tokens})
+        elif sent["label"] == "NEGATIVELY ENGAGEMENT": neg_freq.update({t: weight for t in tokens})
     return pos_freq.most_common(top_n), neg_freq.most_common(top_n)