diff --git a/data-computer/v1/lda.py b/data-computer/v1/lda.py
index b393c50..74c8e46 100755
--- a/data-computer/v1/lda.py
+++ b/data-computer/v1/lda.py
@@ -8,10 +8,6 @@
 import re
 import spacy
 
-# params
-num_topics = 5  # Number of topics
-num_iterations=100 # "epochs" ====> shall it depends of number of docs ? number of topics ?
-
 nlp = spacy.load('en_core_web_sm', disable = ['parser','ner'])
 
 #stopwords
@@ -55,6 +51,19 @@
     all_data.append(data)
 
 
+# params
+n = len(all_data)
+if n< 1001:
+    num_topics = 10
+    num_iterations=100
+elif n < 20001:
+    num_topics = 15
+    num_iterations=150
+else:
+    num_topics = 20
+    num_iterations=200
+
+
 # training LDA
 texts = [tokenize(lemmatize(uniformize(line["value"]))) for line in all_data]
 dictionary = corpora.Dictionary(texts) # Create a tf dictionary, but replace text by an id : [ [(id_token,numb_token),...] , [....] ]. The list represent docs of corpus