Create natural_language_processing.py

models/ai_models/natural_language_processing.py

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+# Import necessary libraries
+import spacy
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import SVC
+
+# Define a class for natural language processing models
+class NaturalLanguageProcessing:
+    def __init__(self, model_type):
+        self.model_type = model_type
+        self.model = self._create_model()
+
+    def _create_model(self):
+        # Create a natural language processing model using SpaCy
+        if self.model_type == 'named_entity_recognition':
+            return spacy.load('en_core_web_sm')
+        elif self.model_type == 'sentiment_analysis':
+            return SVC(kernel='linear', probability=True)
+
+    def recognize_entities(self, text):
+        # Recognize named entities in a text using the named entity recognition model
+        doc = self.model(text)
+        entities = []
+        for ent in doc.ents:
+            entities.append((ent.start_char, ent.end_char, ent.label_))
+        return entities
+
+    def analyze_sentiment(self, text):
+        # Analyze the sentiment of a text using the sentiment analysis model
+        # Implement feature extraction and classification logic here
+        pass
+
+# Define a function to load a natural language processing model
+def load_natural_language_processing(model_path):
+    # Load the natural language processing model from a file
+    model = NaturalLanguageProcessing('named_entity_recognition')
+    model.model = spacy.load(model_path)
+    return model