Feature-based Online Online Emotional Sentiment Analysis

With the rapid development of Internet technology and e-commerce, we have entered the "national online shopping" era. Online reviews of products for other consumers and product feedback provide an important resource. Therefore, how to analysis online consumer reviews of products and related features efficiently and automatically has become a hot topic in the field of sentiment analysis. However, since the Chinese language itself natural diversity and complexity,coupled with the nonstandard language network, the analysis of online reviews research has become more and more difficult.In view of the current sentiment of analysis product online reviews problems that exist in the field, this paper studied the theory and algorithm focus on the aspect-based sentiment analysis.According to the characteristics of product online reviews, we proposed the way to extract the emotional triples of product online reviews based on sentence structure, parts of speech laws. At the last, we trained the neural network algorithms to get the emotional polarity by using the data of the emotional triples. The main work is as follows:1. Studied the theory of aspect-based sentiment analysis of product online reviews.Aspect-based sentiment analysis can extract more accurate information in texts compared with document-level sentiment analysis and sentence-level sentiment analysis. According to the comparison, aspect-based sentiment analysis is the best way to deal with the online review problems.2. Proposed the method that extracting aspect emotional triples of product online reviews based on sentence structure, parts of speech laws. First, collect the areas-dependent aspect set and Internet popular emotional word set. Then, pretreated the review data acquired from Internet and extract the clause patterns. Compared with the Law of the part of speech and the clause patterns, if the clause patterns are the same, then extract the triple. In the end, denoised the aspect emotional triples. This method combines the field-dependent factors and the degree of emotional adverb polar perfectly, and extracted the aspect emotional triples effectively.3. Studied the sentiment analysis of product online reviews based on artificial neural network.First of all, using BP and RBF neural network to train and simulate the model; For neural network algorithm convergence slow, easy to fall into local optimum, global optimization Based on the idea of neural network algorithm improvements. Improving neural network algorithm is a neural network model, each parameter value of global optimization, the hidden layer weights integration, the use of global optimization features determine the most reasonable neural network parameters to improvethe neural network performance of the algorithm. Respectively, for an improved neural network training and simulation; the last four of this article compare the neural network experiments from different dimensions.Simulation results show that, PSO-BP algorithm to deal with commodity online reviews emotional tendency to analyze problems in a higher accuracy rate, but slower convergence; based on PSO-RBF neural network analysis in dealing with emotional problems have a higher accuracy rate and the convergence rate is faster than PSO-BP network.