Research And Application Of Aspect Sentiment Triplet Extraction Method Based On Convolutional Network

With the deep integration of the Internet and 5G communication technology,more and more netizens express their views on product quality and consumer services on platforms such as social media and e-commerce through the mobile Internet.Massive text information containing user sentiment will be generated every moment.Analyzing and extracting the sentimentally inclined information contained in comments is of great value to government organizations,commercial organizations,and individuals.Therefore,sentiment analysis methods began to enter people’s field of vision.Traditional sentiment analysis methods cannot meet the actual needs of users due to their rough analysis granularity.Therefore,researchers began to use more fine-grained aspect-level sentiment classification tasks to predict the sentimental tendency of each aspect in a sentence.With the deepening of research,it is found that although the aspect-level sentiment classification task can predict the sentimental polarity of each aspect,why this aspect obtains such sentiment cannot be intuitively expressed.Therefore,the researchers proposed the task of aspect sentiment triplet extraction,which can output the aspect word,the sentimental polarity for the aspect word,and the opinion word indicating the sentiment by inputting a sentence,to realize the more accurate,and detailed analysis of comment sentences.Since the task was proposed,good results have been achieved,but there many problems and challenges remain.The existing models do not solve the problems of one-to-many and many-to-one relationships between aspect words and opinion words,the incomplete extraction of aspects or opinions composed of multiple words,and the inconsistency of predicted sentiment when decoding sequence tagging methods.Therefore,we proposed two aspect sentiment triplet extraction models to solve the problem,and developed an aspect-level sentiment analysis system for text reviews to verify the effectiveness and practicability of the model.The main work and innovations of this thesis are as follows:(1)The existing models cannot accurately extract aspect-opinion pairs that contain one-tomany and many-to-one relationships,and the use of sequence tag decoding methods will lead to problems such as increased model search space and inconsistent predicted sentiment.In this thesis,we propose a model for aspect sentiment triplet extraction based on convolutional neural network and pointer network.This model first enhances the semantic relationship between aspects and opinions through a convolutional neural network and attention mechanism,and decodes the extracted aspect words and opinion words through a span-based pointer network,effectively addressing the shortcomings of existing models.After multiple experiments on the datasets and comparison with the baseline model,it is concluded that the model can effectively improve the performance of the aspect sentiment triplet extraction task.(2)For the problem that the above model does not use the syntactic structure information in the sentence to enhance the long-distance dependence between aspects and opinions,and does not perform well in extracting aspects and opinions composed of multiple words.In this thesis,we propose a model for aspect sentiment triplet extraction based on multi-channel graph convolutional networks and text span classification.The model first introduces a multi-channel graph convolutional network while considering the syntactic structure complementarity and the correlation of semantic information to enhance the long-range dependencies between aspect words and opinion words.Then,all possible aspects and opinion spans are extracted through span-level text span classification,which solves the problem of incomplete extraction of aspects or opinions composed of multiple words in token-level interactive methods.A large number of experiments and multi-angle analysis studies on four public datasets show that the model can further improve the performance of the aspect sentiment triplet extraction task.(3)Since the current sentiment analysis system can only predict the sentiment tendency but cannot obtain the aspect words and the corresponding opinion words,it is also to verify the validity and practicability of the model proposed in this thesis.Therefore,based on the proposed aspect sentiment triples extraction model,this thesis designs and implements an aspect-level sentiment analysis system for text reviews,so as to meet user needs for more fine-grained sentiments.