Optimizing Automatic Text Classification Approach in Adaptive Online Collaborative Discussion–A Perspective of Attention Mechanism-Based Bi-LSTM

A text semantic classification is an essential approach to recognizing the verbal intention of online learners, empowering reliable understanding, and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as convolutional neural networks and recurrent neural network, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The bidirectional long short-term memory (Bi-LSTM) is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the verbal features and keywords, and the generated keywords were weighed in accordance with the attention mechanism. A total of 12 000 sentences generated in online collaborative discussion activities have been classified into six categories, namely, statement, negotiation, question, management, emotion, and others. Results showed that the classification accuracy of attention-Bi-LSTM reached 81.50%, which is higher than that of the baseline Bi-LSTM model. This study theoretically uncovers the features of collaborative discussion of onliners and practically provides an effective approach to automatic behavior analysis in an online context.