Update modules/semantic_analysis.py

modules/semantic_analysis.py

@@ -183,14 +183,16 @@ def visualize_semantic_relations(doc, lang):
     G = nx.Graph()
     word_freq = defaultdict(int)
     lemma_to_word = {}
+    lemma_to_pos = {}
 
-    # Count frequencies of lemmas and map lemmas to their most common word form
+    # Count frequencies of lemmas and map lemmas to their most common word form and POS
     for token in doc:
         if token.pos_ in ['NOUN', 'VERB']:
             lemma = token.lemma_.lower()
             word_freq[lemma] += 1
             if lemma not in lemma_to_word or token.text.lower() == lemma:
                 lemma_to_word[lemma] = token.text
+            lemma_to_pos[lemma] = token.pos_
 
     # Get top 20 most frequent lemmas
     top_lemmas = [lemma for lemma, _ in sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:20]]
@@ -198,7 +200,7 @@ def visualize_semantic_relations(doc, lang):
     # Add nodes
     for lemma in top_lemmas:
         word = lemma_to_word[lemma]
-        G.add_node(word, pos=doc.vocab[lemma].pos_)
+        G.add_node(word, pos=lemma_to_pos[lemma])
 
     # Add edges
     for token in doc:
@@ -242,7 +244,6 @@ def visualize_semantic_relations(doc, lang):
 
     return fig
 
-    
 ############################################################################################################################################
 def perform_semantic_analysis(text, nlp, lang):
     doc = nlp(text)