Commodity target word oriented emotional tendency analysis method

Abstract

The invention discloses a commodity target word oriented emotional tendency analysis method, which belongs to the field of the analysis processing of online shopping commodity reviews. The method comprises the following four steps that: 1: corpus preprocessing: carrying out word segmentation on a dataset, and converting a category label into a vector form according to a category number; 2: word vector training: training review data subjected to the word segmentation through a CBOW (Continuous Bag-of-Words Model) to obtain a word vector; 3: adopting a neural network structure, and using an LSTM(Long Short Term Memory) network model structure to enable the network to pay attention to whole-sentence contents; and 4: review sentence emotion classification: taking the output of the neural network as the input of a Softmax function to obtain a final result. By use of the method, semantic description in a semantic space is more accurate, the data is trained through the neural network so as to optimize the weight and the offset parameter in the neural network, parameters trained after continuous iteration make a loss value minimum, at the time, the trained parameters are used for traininga test set, and therefore, higher accuracy can be obtained.

Description

technical field [0001] The invention belongs to the field of analysis and processing of online shopping commodity reviews, in particular to a sentiment analysis method for commodity target words. Background technique [0002] With the continuous development of the Internet and the popularity of e-commerce, more and more consumers have begun to post their own comments on related products on e-commerce related websites, and consumers have also become accustomed to checking other people's comments on e-commerce before purchasing goods. of reviews for this product. However, due to the different personal preferences and personal concerns of users, their comments are often very different, and users often express their views on multiple attributes of the product in one comment. In addition, product reviews generally appear in large numbers. It is time-consuming and labor-intensive to manually find the content you care about from these comments. The fast and convenient features of...

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Therefore, these methods are not very good

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