A New Analysis of Web Customer Service Text Classification of Alexa Virtual Assistant Commands Using a Deep Learning Model - Seidakhmet Nurpatsha (Journal of Artificial Intelligence and System Modelling)
Text classification, a significant subfield of Natural Language Processing (NLP), plays a vital role in managing and interpreting the ever-increasing volume of textual data generated by modern technology. As the integration of intelligent systems in daily life grows, the need for accurate and efficient text classification becomes more critical. This study presents a text classification approach focused on processing user commands directed at the Alexa virtual assistant. To evaluate performance, four deep learning-based models are employed: a Simple Neural Network (SNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). All models share a common structure, consisting of three main layers: an embedding layer with 14 dimensions as input, a unique hidden layer specific to each model, and a dense output layer with 18 units corresponding to distinct command classes. Performance metrics such as recall, precision, accuracy, and Area Under the Curve (AUC) are used to assess and compare model effectiveness. The experimental results demonstrate that the CNN model achieves the highest recall (0.86) and accuracy (0.88), making it the most effective model for classifying Alexa commands. Additionally, the SNN model records the highest precision, while CNN attains the best AUC score, highlighting its robust and consistent classification capabilities. These findings suggest that convolutional architectures are particularly well-suited for command classification tasks in virtual assistants, offering promising prospects for improving user interaction, personalization, and responsiveness in intelligent voice-controlled systems.