diff --git a/analyze_arguments.py b/analyze_arguments.py
new file mode 100644
index 0000000..6a9b6b1
--- /dev/null
+++ b/analyze_arguments.py
@@ -0,0 +1,37 @@
+from arango_class import ArangoDB
+from ollama_class import Ollama
+arango = ArangoDB()
+db = arango.db
+
+ollama = Ollama()
+
+# Get all speeches from the 'ev_speeches' collection, containing the 400 speeches filtered out from the European Parliament
+speeches = arango.all_ev_speeches()
+
+negative_arguments = []
+positive_arguments = []
+neutral_arguments = []
+
+for speech in speeches:
+    if speech['llm_sentiment'] == 'negative':
+        negative_arguments.extend(speech['llm_arguments'])
+    elif speech['llm_sentiment'] == 'positive':
+        positive_arguments.extend(speech['llm_arguments'])
+    else:
+        neutral_arguments.extend(speech['llm_arguments'])
+
+negative_arguments = list(set(negative_arguments))
+positive_arguments = list(set(positive_arguments))
+neutral_arguments = list(set(neutral_arguments))
+
+
+for sentiment, arguments in zip(['negative', 'positive', 'neutral'], [negative_arguments, positive_arguments, neutral_arguments]):
+    prompt = f'''Below is a list of arguments related to electric vehicles. They are mostly {sentiment} towards electric vehicles.
+    \n{negative_arguments}\n
+    What arguments are there? Give me a list where you group the arguments into categories.
+    Answer ONLY with the grouped arguments, no greeting or explanation. Keep to the information in the list above.
+    '''
+    grouped_arguments = ollama.generate(prompt=prompt)
+    print(sentiment.upper())
+    print(grouped_arguments)
+    print('-'*30)
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