Product review attribute-level emotion classification method based on rules and neural networks

Abstract

The invention discloses a product review attribute-level emotion classification method based on rules and neural networks. The method includes the steps: firstly, acquiring review data and filtering Chinese participles and stop words from a review text; secondly, screening a product attribute set by the aid of a rule template, building a <attribute and review> sample set, performing emotion tagging on the attribute of each review, and building a <attribute, review and emotion> training set; building a neural network emotion classification model based on bilateral attention, and training the model by the aid of the training set; finally, filtering Chinese participles and stop words from testing data, screening a product attribute set, building a <attribute and review> testing set, and performing emotion classification by the aid of an emotion classification model. According to the method, attribute emotion category forecasting accuracy can be greatly and effectively improved by the aidof context information of attributes in the reviews.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to an attribute-level sentiment analysis method for product reviews. Background technique [0002] Under the premise of the rapid development of the Internet, e-commerce, as an emerging field of the Internet, has achieved considerable development. More and more users are purchasing online commodities through different e-commerce websites, resulting in massive consumer purchase reviews. Faced with a large number of shopping reviews, it is impossible for consumers to read them one by one, and merchants are eager to analyze and sort out product reviews. Opinion mining and sentiment analysis technology for product reviews are not only academic frontier issues and hot research issues in the field of natural language processing and sentiment analysis, but also important issues to be solved in application fields such as e-commerce shopping platforms, which have immeasurable applicat...

AI Technical Summary

Problems solved by technology

The traditional method uses artificial rules to judge attribute-level emotions, which requires high labor costs and low efficiency of emotion judgments

In recent years, some scholars have proposed to solve this problem based on machine learning methods, such as logistic regression model, support vector machine model, neural network model, etc., but these models cannot fully express the semantic and emotional relationship between attribute words and context

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