diff --git a/Jupyter/Sentiment_Company_Matching/Drop_News_from_Mongo.ipynb b/Jupyter/Sentiment_Company_Matching/Drop_News_from_Mongo.ipynb
new file mode 100644
index 0000000..bc526bf
--- /dev/null
+++ b/Jupyter/Sentiment_Company_Matching/Drop_News_from_Mongo.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebook to drop news article from MongoDB, because these articles do not match"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import aki_prj23_transparenzregister.utils.mongo.connector as conn\n",
+    "from aki_prj23_transparenzregister.config.config_providers import JsonFileConfigProvider\n",
+    "import aki_prj23_transparenzregister.utils.mongo.news_mongo_service as news\n",
+    "import aki_prj23_transparenzregister.utils.mongo.company_mongo_service as comps\n",
+    "from tqdm import tqdm\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "202"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# optional: LAden der ObjectIDs\n",
+    "with open(\"ObjectID2\", \"rb\") as fp:  # Unpickling\n",
+    "    compListLoaded = pickle.load(fp)\n",
+    "\n",
+    "len(compListLoaded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mongo Connect: create connection string and connect\n",
+    "config_provider = JsonFileConfigProvider(\"../../secrets.json\")\n",
+    "engine = config_provider.get_mongo_connection_string()\n",
+    "engine.database = \"transparenzregister\"\n",
+    "connector = conn.MongoConnector(engine)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process all documents in news collection and check if attribute 'companies' is existing\n",
+    "\n",
+    "# Read data from database\n",
+    "NERObj = news.MongoNewsService(connector)\n",
+    "allNER = NERObj.get_all()\n",
+    "\n",
+    "# Create a cursor which has all unprogressed articles; articles without the attribute 'companies'\n",
+    "CursorNERNames = NERObj.collection.find({\"companies\": {\"$exists\": True}})\n",
+    "documentsNER = list(CursorNERNames)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3055"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(documentsNER)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 3055/3055 [00:04<00:00, 744.27it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop all documents, which have no matching from Rapidfuzz\n",
+    "if len(documentsNER) > 0:\n",
+    "    for document in tqdm(documentsNER):\n",
+    "        if document[\"_id\"] not in compListLoaded:\n",
+    "            # print(\"Doc found not in list!!!\")\n",
+    "\n",
+    "            NERObj.collection.delete_one({\"_id\": document[\"_id\"]})\n",
+    "else:\n",
+    "    print(\"No documents found.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aki-prj23-transparenzregister-eMuJN1BX-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/aki_prj23_transparenzregister/ai/sentiment_service.py b/src/aki_prj23_transparenzregister/ai/sentiment_service.py
index 8bdd839..00c1709 100644
--- a/src/aki_prj23_transparenzregister/ai/sentiment_service.py
+++ b/src/aki_prj23_transparenzregister/ai/sentiment_service.py
@@ -70,7 +70,7 @@ class SentimentAnalysisService:
         # loading the sentiment model(~ 436MB) for transformer
         self.sentiment_analyzer = pipeline(
             "sentiment-analysis",
-            model="oliverguhr/german-sentiment-bert",
+            model="bardsai/finance-sentiment-de-base",
             truncation=True,
         )