Fine-grained Sentiment Analysis Of Product Review Short Text Based On Lifelong Learning

The rapid development of online shopping has brought massive online reviews,mining that to users and businesses has a great value.With the increasing demands of users and merchants,it is important to conduct more fine-grained research on product reviews.However,the characteristics of products evaluated in reviews not always explicitly mentioned,the form of product reviews has become so shorter that the current status of online reviews has the problem of sparse text and insufficient contextual information.Therefore,the automated task of analyzing customer reviews to obtain a more fine-grained understanding still faces many challenges.The research objective of this thesis is to improve the accuracy of fine-grained sentiment analysis in product review short texts.The main work of this paper is as follows:(1)This thesis introduces the related technology of sentiment analysis for product reviews.It mainly introduces the background and working principle of JST model and ASUM model while analyzing their advantages and disadvantages as well as their application scenarios.Finally,the unsupervised methods of product review feature extraction are introduced where their advantages and disadvantages also introduced.(2)In order to solve the problem of short text scarcity,the task of identifying feature words and affective words,while classifying general affective words and specific affective words processed under the same framework,a short-text attribute-level sentiment analysis method based on lifelong learning is proposed.By extending the WSTM model,Maxent-JAWSTM(Joint Aspect-based WordPair Sentiment Topic Model),is proposed firstly.Then,to improve the consistency and correctness of the topics obtained from the model sampling,lifelong learning is combined with the MaxentJAWSTM model.Finally,experimental comparison shows the effectiveness of the proposed method for fine-grained sentiment analysis of short texts.(3)To solve the problem that existing researches on implicit feature recognition fail to consider the guidance of non-point words in the absence of point words,an implicit feature recognition method based on domain feature deicers is proposed.This method uses the constructed multi-word thematic sentiment association model to mine the indicator words under the feature category of the display comment sentences in a specific domain.Then,in the process of identifying implicit features,the Word2 vec is introduced as the standard to measure the semantic correlation.According to the type of clue word,it can realize the assignment of implicit features according to the situation.