Research On Text Sentiment Analysis Of Public Opinion Based On Recurrent Neural Network

Text sentiment analysis,as one of the research hotspots in the field of natural language processing,is gaining more and more attention in recent years,its aim is to automatically extract and summarize subjective sentiment information from text.Existing methods of sentiment analysis can be divided into approach based on sentiment lexicon and approach based on machine learning.The performance of sentiment lexicon based approach heavily depends on the quality of sentiment lexicons,while it is very difficult to build a broad and accurate sentiment lexicon.As for the machine learning based approach,it depends on the selection and construction of features,while traditional feature representation methods can not keep the semantic information of the text well.With the application of deep learning in computer vision,deep learning model has been proved that it has great advantage in feature extraction,in which recurrent neural network is very suitable for dealing with text and other sequence data benefits from its special recurrent chain structure,thus it has been widely used in the field of natural language processing.This paper mainly studies the recurrent neural network based sentiment analysis approach.The contents of this work can be divided into two parts:1)The application of recurrent neural network in traditional sentiment analysis tasks.Considering the fact that the sentiment polarity of a text is largely depends on the emotional words in it,focusing in these emotional words is expected to improve the performance of sentiment classification.Aim at the overlook of emotional words in the present recurrent neural network model used for sentiment analysis,this paper propose a sentiment analysis attention model based on recurrent neural network(RNN-Attention).After attention mechanism is introduced in the model,more attention is paid to the emotional words in text.Finally,experiments are conducted on the NLPCC 2014 sentiment analysis dataset and IMDB movie review dataset,the results show that our model can improve the performance of text sentiment classification.2)Recurrent neural network based model for target-dependent sentiment analysis tasks.Target-dependent sentiment analysis classifies the sentiment polarity toward a target entity mention in a given text.At present,most of sentiment analysis approach is focus on the traditional sentiment analysis task,namely directly analyzing the sentiment polarity of a given text.When traditional sentiment analysis approach is used to deal with target-dependent sentiment analysis task,since the target information is not be considered,it will lead to the wrong judgment.To address this problem,RNN-Attention-T model based on RNN-Attention model is proposed,which introduces the information of the target while modeling text.Furthermore,considering the fact that the influence of the preceding context and following context of a target to text sentiment polarity is often different,a model that respectively models both the preceding context and following context,which is called RNN-Attention-C model,is proposed.Experimental results show that,compared with the current method for target-dependent sentiment analysis task,the improved models proposed in this paper can get better classification accuracy without using syntactic parser or external sentiment lexicons.