Create sentiment_analysis.py

natural_language_processing/sentiment_analysis.py

@@ -0,0 +1,19 @@
+import tensorflow as tf
+
+def train_sentiment_analysis_model(data, labels, config):
+    # Define the model architecture
+    model = tf.keras.Sequential([
+        tf.keras.layers.Embedding(config['vocab_size'], config['embedding_dim'], input_length=config['max_sequence_length']),
+        tf.keras.layers.GlobalAveragePooling1D(),
+        tf.keras.layers.Dense(config['output_units'], activation=config['output_activation'])
+    ])
+
+    # Compile the model
+    model.compile(optimizer=config['optimizer'],
+                  loss=config['loss'],
+                  metrics=[config['metric']])
+
+    # Train the model
+    model.fit(data, labels, epochs=config['epochs'], batch_size=config['batch_size'])
+
+    return model