A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification

The aspect-based sentiment analysis (ABSA) task consists of two closely related subtasks: aspect extraction and sentiment classification. However, the majority of previous studies looked into each task separately, limiting their effectiveness. In contrast, the integration of aspect extraction and sentiment classification into a single model improves results. The main focus in this work is to manage these two tasks into a new collapsed model. The proposed model relies upon the bidirectional long short-term memory (Bi-LSTM) architecture. On the one hand, it combines a multi-channel convolution layer with an optimization method for handling the aspect extraction task. On the other hand, it includes an attention mechanism based on the residual block and aspect position information for predicting the appropriate opinion orientation of an aspect. The experimental results demonstrate that the model achieved the best performance.