Fine-grained Emotional Analysis Of Multi-channel Restaurant Reviews Based On In-depth Learning

As an important part of natural language processing,emotion analysis is widely used in today’s rapid development of e-commerce.However,traditional sentiment analysis utilizes the method of sentiment dictionaries to provide a single emotional polarity for the entire comprehensive comment,neglecting the detailed analysis of multiple emotions from different perspectives.Therefore,in order to further accurately and efficiently analyze the emotions of different aspects of comprehensive reviews,this article constructs a multi-channel finegrained sentiment analysis model for restaurant reviews based on deep learning methods,and conducts research from two perspectives:optimizing the fine-grained sentiment analysis model and feature extraction accuracy.Among them,the research on sentiment analysis models focuses on granular emotional polarity in food and beverage reviews,using a multi-channel approach to improve the accuracy of sentiment analysis at each granularity,and then targeted exploration of the potential value in comprehensive reviews;The research on optimizing feature extraction is to improve the Bert pre training model,construct a vector table of restaurant comment words,introduce open-source knowledge graph,and improve the extraction effect of implicit features.The main work of this article is:(1)A Bert-BiSRU-Att single-channel fine-grained emotional analysis model for restaurant reviews was constructed.In order to solve the problem of multi-dimensional emotion analysis with multi granularity and multi polarity,this paper proposes a Bert-BiSRU-Att model,which inputs the dynamic word vector obtained by Bert pre training model into a single channel BiSRU-Att to obtain rich semantic information.Create a foundation for optimizing the emotional analysis model in the next stage.(2)A Bert-BiSRU-Att-Text CNN multi-channel fine-grained emotional analysis model for restaurant reviews was constructed.Change the input method of the single channel model,input the phrase and sentence vectors into the BiSRU-Att,Text CNN,and sentence vector channels in three channels,preserve the feature information extracted from each channel,and the semantic relationship between the subject word and the emotional word.Finally,output 13 granularity emotional polarity.The experimental results show that the multi-channel model proposed in this paper significantly improves the accuracy of fine-grained emotional analysis compared to other models.(3)A KW-Bert-BiSRU-At-Text CNN multi-channel fine-grained emotional analysis model for restaurant reviews was constructed.In order to further improve the accuracy of emotion analysis,the pre training model was first optimized,and the pre training model K-Bert with an open source knowledge map was introduced.Special words were labeled to add additional feature information.Then,based on the food and beverage review word vector table,word vectors were tested for similarity using kd-tree,thereby efficiently identifying words in special areas and enriching the semantic and grammatical information of downstream emotion analysis models;Then,the improved pre training model and multi-channel emotional analysis model are applied to the food and beverage review data set to further improve the accuracy of emotional analysis;Finally,through comparative experiments,the validity of the KW-Bert-BiSRU-At-Text CNN model proposed in this paper is verified.