diff --git a/src/aki_prj23_transparenzregister/ai/ner_pipeline.py b/src/aki_prj23_transparenzregister/ai/ner_pipeline.py
index c90f7c5..f98ef6a 100644
--- a/src/aki_prj23_transparenzregister/ai/ner_pipeline.py
+++ b/src/aki_prj23_transparenzregister/ai/ner_pipeline.py
@@ -67,15 +67,15 @@ class EntityPipeline:
             ner_service_func = ner_service_instance.ner_transformer
         else:
             raise ValueError
-        if len(documents) > 0:
-            for document in tqdm(documents):
-                ents = ner_service_func(document, "ORG", doc_attrib)
-                self.news_obj.collection.update_one(
-                    {"_id": document["_id"]},
-                    {"$set": {"companies": ents}},
-                )
-        else:
+        if not documents:
             logger.info("No documents found.")
+            return
+        for document in tqdm(documents):
+            ents = ner_service_func(document, "ORG", doc_attrib)
+            self.news_obj.collection.update_one(
+                {"_id": document["_id"]},
+                {"$set": {"companies": ents}},
+            )
 
 
 def execute_ner(config_provider: ConfigProvider) -> None:
diff --git a/src/aki_prj23_transparenzregister/ai/sentiment_pipeline.py b/src/aki_prj23_transparenzregister/ai/sentiment_pipeline.py
index 88c9a91..31d5f56 100644
--- a/src/aki_prj23_transparenzregister/ai/sentiment_pipeline.py
+++ b/src/aki_prj23_transparenzregister/ai/sentiment_pipeline.py
@@ -43,33 +43,31 @@ class SentimentPipeline:
             {"sentiment": {"$exists": False}}
         )
         documents = list(cursor_unprocessed)
-        logger.info(f"Documents to be processed: {cursor_unprocessed}")
-
-        if len(documents) > 0:
-            for document in tqdm(documents):
-                text = document[doc_attrib]
-
-                # Determine sentiment analysis service based on config
-                if sentiment_method == "spacy":
-                    selected_service = sentiment_service.SentimentAnalysisService(
-                        use_spacy=True, use_transformer=False
-                    )
-                    sentiment_service_func = selected_service.sentiment_spacy
-
-                elif sentiment_method == "transformer":
-                    selected_service = sentiment_service.SentimentAnalysisService(
-                        use_spacy=False, use_transformer=True
-                    )
-                    sentiment_service_func = selected_service.sentiment_transformer
-
-                sents = sentiment_service_func(text)
-                sentiment = {"label": sents[0], "score": sents[1]}
-                self.news_obj.collection.update_one(
-                    {"_id": document["_id"]},
-                    {"$set": {"sentiment": sentiment}},
-                )
-        else:
+        if not documents:
             logger.info("No documents found.")
+            return
+        for document in tqdm(documents):
+            text = document[doc_attrib]
+
+            # Determine sentiment analysis service based on config
+            if sentiment_method == "spacy":
+                selected_service = sentiment_service.SentimentAnalysisService(
+                    use_spacy=True, use_transformer=False
+                )
+                sentiment_service_func = selected_service.sentiment_spacy
+
+            elif sentiment_method == "transformer":
+                selected_service = sentiment_service.SentimentAnalysisService(
+                    use_spacy=False, use_transformer=True
+                )
+                sentiment_service_func = selected_service.sentiment_transformer
+
+            sents = sentiment_service_func(text)
+            sentiment = {"label": sents[0], "score": sents[1]}
+            self.news_obj.collection.update_one(
+                {"_id": document["_id"]},
+                {"$set": {"sentiment": sentiment}},
+            )
 
 
 def execute_sentiment(config_provider: ConfigProvider) -> None: