diff --git a/Text_classification_of_newspaper_clippings_notebook.ipynb b/Text_classification_of_newspaper_clippings_notebook.ipynb
index cbc9e55..0d82dcc 100644
--- a/Text_classification_of_newspaper_clippings_notebook.ipynb
+++ b/Text_classification_of_newspaper_clippings_notebook.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "For classification, topic modelling (LDA) was chosen because it showed the best performance in classification (after experiments with word embeddings or LDA and word embeddings combined). LDA provides a way to group documents by topic and perform similarity searches and improve precision. Thanks to sklearn, it is relatively easy to test different classifiers for a given topic classification task. Logistic regression was chosen as binary classifier. \n",
     "\n",
-    "*Following graph demonstrates the distribution of the pre-defined categories in newspaper clippings of seleceted Austrian Newspapers (~1000 clippings) on the topic of emigration.* \n",
+    "*Following graph demonstrates the distribution of the pre-defined categories in newspaper clippings of seleceted Austrian Newspapers (790 clippings) on the topic of emigration.* \n",
     "\n",
     "![Collection on the topic of Emigration](images/categories.PNG)\n",
     "\n",