diff --git a/schemas.py b/schemas.py
index d9efd95..954a90f 100644
--- a/schemas.py
+++ b/schemas.py
@@ -42,6 +42,7 @@ class TripRequest(BaseModel):
 
 class Review(BaseModel):
     city: str
+    airport: str
     address: str
     name_ru: str
 
diff --git a/sity/conversion_sity.py b/sity/conversion_sity.py
index 9357a0b..e921765 100644
--- a/sity/conversion_sity.py
+++ b/sity/conversion_sity.py
@@ -4,18 +4,20 @@ import pandas as pd
 class CityFilter:
     def __init__(self, json_file_path):
         self.json_file_path = json_file_path
-        self.cities_set = self.load_and_extract_cities()
+        self.cities_airports = self.load_and_extract_cities_airports()
 
-    def load_and_extract_cities(self):
+    def load_and_extract_cities_airports(self):
         data = self.load_json(self.json_file_path)
-        cities_set = set()
+        cities_airports = {}
         for entry in data:
             parts = entry['label'].split(',')
             if len(parts) > 1:
-                city1 = parts[0].strip().split()[0] if parts[0].strip().split() else ''
-                city2 = parts[1].strip().split()[0] if parts[1].strip().split() else ''
-                cities_set.update([city1, city2])
-        return cities_set
+                city1 = ' '.join(parts[0].strip().split()[:2])  # Возьмем первые два слова
+                city2 = ' '.join(parts[1].strip().split()[:2])  # Возьмем первые два слова
+                full_address = entry['label'].strip()
+                cities_airports[city1] = full_address
+                cities_airports[city2] = full_address
+        return cities_airports
 
     @staticmethod
     def load_json(file_path):
@@ -24,23 +26,26 @@ class CityFilter:
         return data
 
     @staticmethod
-    def find_city(address, cities_set):
+    def find_city_and_airport(address, cities_airports):
         parts = address.split(',')
         for part in parts:
             words = part.strip().split()
-            for word in words:
-                if word in cities_set:
-                    return word
-        return None
+            for i in range(len(words)):
+                city_1 = ' '.join(words[i:i+1])
+                city_2 = ' '.join(words[i:i+2])
+                if city_1 in cities_airports:
+                    return city_1, cities_airports[city_1]
+                if city_2 in cities_airports:
+                    return city_2, cities_airports[city_2]
+        return None, None
 
     def filter_cities_in_csv(self, csv_file_path, output_path):
         df = pd.read_csv(csv_file_path)
-        df['city'] = df['address'].apply(lambda x: self.find_city(x, self.cities_set))
+        df['city'], df['airport'] = zip(*df['address'].apply(lambda x: self.find_city_and_airport(x, self.cities_airports)))
         df = df[df['city'].notnull()]
         df.to_csv(output_path, index=False)
         print(f"Filtered entries:\n{df.head(15)}")
 
-
 # Пример использования класса
 json_file_path = 'airports.json'
 csv_file_path_positive = '../neural_network/dataset/filtered/filtered_dataset_positive.csv'