## 6 Application of Methodology and Modeling to Museum Visitors
CNNs can extract and analyze hierarchical data features, beneficial for interpreting emotional nuances. * Input Layer: Takes normalized responses as a feature map, allowing CNN layers to identify patterns. * Convolutional Layers: Apply filters to capture spatial dependencies in data, with ReLU activation functions to enhance data representation. * Pooling Layers: Downsample data while retaining key emotional features for efficient processing. * Fully Connected Layers: Integrate the features extracted by convolutional and pooling layers for final emotional representation. * Output Layer: Provides trivalent predictions (\(-1\), \(0\), \(1\)) representing emotional states and calculates dominance and emotional metrics. Here is a Python code implementing the CNN-based emotional analysis strategy. This code uses Keras, a high-level neural network API, running on top of TensorFlow. The following code is structured to preprocess the input data, build a CNN model, train it with labeled data, and make predictions. The CNN model has an input layer, convolutional layers, pooling layers, and a fully connected layer, with an output layer that provides trivalent predictions (\(-1\), \(0\), \(1\)) for emotional states. For this example, let's assume we have data prepared in a suitable format, as well as labeled emotional states. Code Outline:  
1. Preprocess Input Data: Load and normalize the responses. 2. Build CNN Model: Define a CNN architecture with convolutional, pooling, and fully connected layers. 3. Compile and Train Model: Train with labeled data. 4. Predict Emotional States: Make predictions of new visitor responses. In the following, we present the relevant part of the Python code. import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense from tensorflow.keras.optimizers import Adam from tensorflow.keras.utils import to_categorical from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler  
Assume data is pre-loaded as'responses' (input data) and 'labels' (output labels) #'responses' is a 2D array where each row represents a visitor's response features # 'labels' is a 1D array with trivalent emotional state labels (\(-1\), \(0\), \(1\)) # e.g., responses = np.array([[...], [...]...]) # e.g., labels = np.array([\(-1\), \(0\), \(1\),...])  
Step 1: Preprocess Input Data # Normalize input data scaler = StandardScaler() responses_normalized = scaler.fit_transform(responses)  
Convert trivalent labels to categorical (for softmax output layer) labels_categorical = to_categorical(labels + 1, \(3\)) # Shift labels (\(-1\), \(0\), \(1\)) to (\(0\), \(1\), \(2\))  
Reshape input for CNN (assuming a 1D CNN as data is a sequence of features per visitor) responses_normalized = responses_normalized.reshape((responses_normalized.shape[0], responses_normalized.shape[1], 1))  
Split data into training and test sets X_train, X_test, y_train, y_test = train_test_split(responses_normalized, labels_categorical, test_size = 0.2, random_state = 42)Step 2: Build CNN Model model = Sequential([ Conv1D(filters = 32, kernel_size = 3, activation ='relu', input_shape = (re spans_normalized.shape[1], 1)), MaxPooling1D(pool_size = 2), Conv1D(filters = 64, kernel_size = 3, activation ='relu'), MaxPooling1D(pool_size = 2), Flatten(), Dense(64, activation ='relu'), Dense(3, activation ='softmax') # 3 output units for trivalent emotional states (- 1, 0, 1) ]
Step 3: Compile and Train Model model.compile(optimizer = Adam(learning_rate = 0.001), loss = 'categorical_crossentro py', metrics = ['accuracy'])
Train the model history = model.fit(X_train, y_train, epochs = 20, batch_size = 32, valida tion_data = (X_test, y_test))