Research On Weibo Opinion Sentence Recognition And Specific Target Sentiment Analysis

With the development of social media,many new social platforms have appeared on the Internet.With the help of these social platforms,such as blogs,microblogs and shopping websites,netizens can express their views and communicate with other netizens in real time.Due to the development of the Internet,the information generated by users on these platforms is massive every day.How to mine these information effectively is a meaningful thing.The research direction of this paper is emotional analysis.Microblog is an important platform for user communication.Through the analysis of microblog data,it can meet the needs of current public opinion analysis and commercial application.Based on neural network and support vector machine,this paper studies the problem of opinion sentence identification and Target-specific sentiment analysis.After research and discussion,as well as the summary of the existing work,this paper focuses on the methods of opinion sentence identification and Target-specific sentiment analysis,mainly including the following research:1.Microblog opinion sentence identification based on self attention bidirectional gating loop unit and support vector machineTo solve the problem that existing methods rely on artificial feature selection and sparse extracted data,this paper proposes a method based on the combination of Bi GRU and SVM,which can automatically learn the effective features in sentences.By introducing self attention,the model has more attention weight to the key features in the sentence,and then generates a two-dimensional sentence embedding matrix.Each vector in the matrix represents different parts of the sentence.In this method,the sentence embedding matrix is obtained by Bi GRU combined with self attention.The sentence embedding matrix is transformed into vector form,and then the sentence embedding vector needs to be input to SVM classifier for training,and finally the SVM classifier outputs the classification results.Compared with SVM and Bi LSTM,this method is based on the fusion of the above two methods,which can effectively improve the precision of opinion sentence recognition.2.Research on Target-specific sentiment analysis based on segmented long short-term memory networks and gated convolutional neural networkTarget-specific sentiment analysis is a very challenging work.The main difficulty lies in how to strengthen the relationship between specific target and context,and also in considering the influence of target words on the emotional polarity of sentences in different contexts.In view of the above considerations,we use the combination of long short-term memory networks and gated convolutional neural network to analyze the Target-specific sentiment analysis.In this method,the target word vector and the word vector are spliced to get the fusion vector of the two,and then input into LSTM to get the hidden layer state with the target word information;then input the hidden layer state into the gated convolutional neural network,then gated convolutional neural network outputs the emotional characteristics related to the target word,and finally softmax outputs the classified results.Compared with the gated convolutional neural network,this method has a higher precision.Using the research results,a prototype system based on Microblog opinion sentence identification and Target-specific sentiment analysis is designed and developed.The tools and system framework needed for system construction are introduced,and the design process of the system is described in detail.The micro blog opinion sentence identification and Target-specific sentiment analysis are realized.