Study On Aspect-level Sentiment Analysis With Deep Learning

With the development of social networks and e-commerce platforms,more and more people are willing to share online comments about shopping,tourism,services and other fields.Those texts with personal and emotional attitudes are valuable for mining.In the field of sentiment analysis,document -level and sentence -level sen timent analysis only concentrate on sentiment polarity upon the whole text,while the detailed sentiment or opinions on certain entities or attributes in the text are ignored.Thus,the task of aspect- level sentiment analysis is proposed,here the aspect referes to the entity or attribute.Deep learning has obtained great success in the artificial intelligence fields and it also provides new solutions for aspect -level sentiment analysis tasks.This paper proposes efficient solutions to some existing problems in aspect -level sentiment analysis with deep learning.Firstly,most of the existing aspect-level sentiment classification models predict the sentiment based on the entire sentence,which might introduce noise information irrele-vant to the sentiment of aspect.This paper proposes a novel neural network model based on reinforcement learning framework to adaptively extract aspect-related segments and apply them for aspect-level sentiment classification.The experimental results show that the proposed method can effectively extract the segment oriented to the aspect,and thus improve the classification performance.In addition,case studies are used to intuitively understand why the proposed model is suitable for aspect-level sentiment classification.Secondly,since the existing aspect-level sentiment analysis models are negligent in utilizing the relationship among aspect term extraction,opinion term extraction and aspect-level sentiment classification,this paper proposes an end-to-end aspect-level sentiment analysis model.This model can not only learn the relationship between sub-tasks,but also solve the problems that one aspect with multiple opinion words and one opinion term for multiple aspects.In the end,experiments are conducted on three stan-dard English datasets to verify the performance of the model.Experimental results show that the proposed model can achieve comparable or better results than strong baseline models.Finally,to explore the applicability and generalization of the proposed model,this paper develops an aspect -level sentiment analysis system.The system consists of on-line demo,application display and data analysis.Fisrtly,the online demo module can help users complete aspect-level sentiment analysis online.Furthermore,the applica-tion display module provides the function of reviews classification,it can help users to understand the characteristics of the product and conduct sample studies of proposed model.Eventually,the data analysis module can quickly complete the data analysis and visualization,including distribution information such as aspect terms,opinion terms and sentiment polarities,it can be used to better understand the data distribution and users' attitudes on every aspect of the product.