Research On Fine-grained Sentiment Analysis Algorithm With Deep Learning

Text sentiment analysis studies the emotions,opinions,and attitudes that people express in texts.Fine-grained sentiment analysis is one of the sub-fields.Compared with the coarse-grained sentiment analysis that studies the overall sentiment of the text,the fine-grained sentiment analysis directly analyzes the sentiment of the entities in the text,which has more practical value.The goal of fine-grained sentiment analysis is to extract entities in the text and classify the sentiment tendency of each entity according to the sentiment in the sentence.According to the extracted items,it can be divided into three sub-tasks: aspect item extraction,opinion item extraction and aspect based sentiment classification.This paper mainly studies aspect sentiment classification and aspect sentiment triplet extraction task combining three sub-tasks.For aspect based sentiment classification,most of the researches accomplish this work by modeling the global context using attention mechanism and external semantic knowledge.The sentiment polarity of an aspect often depends on the local context that is highly related to the aspect,but most models focus too much attention on the global context,which makes the amount of parameters of the model generally larger,resulting in an increase in the amount of computation.To this end,this paper proposes a light weight network model based on the multi-head attention mechanism,local and global feature fusion network model.The local context is obtained according to the semantic correlation distance with the aspect term,and the feature extraction is carried out separately from the global context,and the extraction results of the two are combined to improve the classification accuracy.Experimental results on three standard datasets show that the model achieves better results than other aspect-based sentiment classification algorithms with a small amount of parameters.For aspect sentiment triplet extraction,in most of the existing methods,this type of task are regarded as some sub-tasks,and the researchers complete the task by a pipeline which composed of the sub-tasks.However,the methods based on pipeline will be affected by error propagation,and it is not easy to use the methods in practice.To this end,this paper proposes a word-pair relation learning method,which transforms the aspect sentiment triplet extraction task into an end-to-end word-pair relation learning task.The word-pair relation in the sentence is uniformly labeled to represent all triplets,and the attention map of multi-head self-attention is used as the word-pair relation output,and all triplets in the sentence can be obtained through the word-pair relation decoder.Experiments on four standard datasets show that the method has better performance.