Research On Aspect Category Sentiment Classification Based On Deep Learning Models

Sentiment classification is a classic task of NLP having important application value and many application scenarios.Aspect-level sentiment classification is fine-grained with the goal being to predict the sentiment polarity of a sentence to be conveyed on a specific aspect.Due to the fact that most users comment on aspects without explicitly naming the aspect terms,aspect category sentiment classification digs deeper information being closer to actual needs compared to the other.Based on deep learning knowledge and text mining technology,this paper takes aspect category extraction and aspect category sentiment classification as research tasks and improves related neural network models.For the task of extracting aspect category,ABAE model utilized the characteristic information of the aspects to improve the coherence.Considering ABAE model was insufficient and still had room for improvement,this paper introduces self-attention mechanism and proposes SABAE model to make the representation of each position of the review text have global semantic information so that the model can learn word embedding with contextual semantic information,and introduces relative position representation to propose SABAEwith RPR model to simulate the order of words fixing the problem of self-attention mechanism not considering timing information.For the aspect category sentiment classification task,GCAE model trained in parallel was proposed,which can extract aspect category features and sentiment features separately.Considering the aspect category used in GCAE model is randomly initialized and context-free,this paper correlates aspect category extraction and aspect category sentiment classification,introduces trained aspect embedding matrix and word embedding matrix into GCAE model.Besides,this paper also introduces k-max pooling trying to improve the sentiment classification accuracy.Ablation experiment results on standard Sem Eval dataset showed that the aspect categories extracted by the SABAE model have higher coherence and better interpretability,adoption of corresponding word embeddings and aspect embeddings to initialize the inputs of GCAE model is effective to improve the accuracy of sentiment classification.