Research And Implementation Of Aspect-based Sentiment Analysis Technology Based On Comment Text

As the public is more and more willing to post comments on the Internet,these emotionally colored texts have mining value,and text-based sentiment analysis technology has also become one of the important research directions in the field of natural language processing.Different from document-level and sentence-level sentiment analysis that can only analyze the overall sentiment polarity,as a fine-grained sentiment analysis,aspect-based sentiment analysis is an accurate judgment of sentiment tendency for a specific attribute.It mainly includes three sub-tasks:attribute extraction,sentiment word extraction and sentiment polarity prediction.At present,each dataset in the attribute-level sentiment analysis task contains a small amount of data,and labeled datasets are scarce.In addition,most methods implement the tasks of attribute extraction,sentiment word extraction,and sentiment polarity prediction in a pipelinebased manner,which has the problem of error propagation to a certain extent.Based on the above background,this paper mainly studies the aspect-based sentiment analysis model based on transfer learning and the end-to-end aspect-based sentiment analysis model based on knowledge distillation.The main work can be summarized as follows:First,to address the problem of inadequate labeled data in aspectbased sentiment analysis tasks,this paper proposes an aspect-based sentiment analysis model based on transfer learning,which is constructed by using task-specific target domains and additional product domain data with high correlation corpus,fine-tuned on top of pre-trained language models for extended learning of domain knowledge,and then transferred to aspect-based sentiment analysis tasks.Through some experiments,the model proposed in this paper has been confirmed to have some utility in aspect-based sentiment analysis.Secondly,in view of the problem of error propagation caused by the pipeline method,in order to make better use of the relationship between the two sub-tasks of aspect extraction and sentiment classification,this paper proposes an end-to-end aspect-based sentiment analysis model based on BERT,and then integrates the self-distillation mechanism compresses the model and speeds up inference.The experimental results show that the method proposed in this paper obtains a competitive accuracy rate,and achieves a faster inference effect than the benchmark method,achieving a balance between inference accuracy and speed.Finally,in order to explore the applicability and generalization ability of the proposed end-to-end aspect-based sentiment analysis model,this paper designs and develops an aspect-based sentiment analysis prototype system for English comment texts on the basis of the model.Aspect term extraction and sentiment polarity prediction and comment screening functions,that is,fine-grained evaluation and analysis of all aspects of the comment text.This paper introduces the architecture design and implementation of functional modules in detail.The validity of the algorithm model and the applicability of the system are verified by testing the aspect-based sentiment analysis system.