Aspect-level Emotion Classification Method Integrating Multiple Textual Information and Attention Mechanism
In order to solve the problem that the current sentiment classification methods do not fully utilize text information and lack consideration of user preferences,resulting in low sentiment classification accuracy,this paper introduces an attention mechanism to deal with multiple texts,and uses the SRNN model to fully extract Based on the hidden features of text,an aspect-lev-el sentiment classification method that fuses multiple textual information and attention mechanisms is proposed.This method takes the e-commerce platform as the research object,comprehensively uses the product introduction text and user comment text,firstly uses the attention mechanism to interact with the two text information,and obtains the representation vector that integrates multiple texts.The information is processed to fully extract the hidden features of the text.Finally,the different aspects involved in the com-ment information are trained with the corresponding aspect processing module,and the most interesting aspect is obtained according to the user's preference,and the feature vector is inputted into the aspect processing module,perform aspect-level sentiment polari-ty calculation,and finally obtain sentiment classification results.Compared with the current mainstream methods based on LSTM and CNN,the method proposed in this paper is significantly improved in accuracy and F1 value.
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