Deep learning commodity comment text sentiment tendency analysis method

Abstract

The invention relates to a deep learning commodity comment text sentiment tendency analysis method. The method comprises the following steps of processing the text to obtain the simplest text; dividing the simplest text into a training set and a test set; obtaining a word sequence according to the training set and setting an emotion feature with an emotion weight value, adjusting the emotion weight value to obtain a corrected emotion weight value; according to the word sequence, obtaining a plurality of word group sequences according to different composition word combination modes; calculatinga correction emotion weight value in the word group sequence to obtain an emotion label of the text; and comparing the emotion label with the human emotion evaluation to obtain a final emotion weightvalue and a final corrected emotion weight value, executing the operation on all texts in the training set, and obtaining an emotion model according to the final emotion weight value and the final corrected emotion weight value to verify the emotion analysis of the test set.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a method for analyzing sentiment tendency of commodity review texts through deep learning. Background technique [0002] With the healthy development of e-commerce in my country, the traffic bonus period has passed, and customer costs are getting higher and higher. How e-commerce companies identify customer consumption preferences, carry out precise marketing, and reduce competition costs is a must for every company. Commodity review data is the customer's evaluation of product quality, price, service, etc. after the e-commerce transaction is completed. Commodity review data has become an important source of information for enterprises to obtain customer consumption preferences and carry out precise marketing. This kind of evaluation set often has a strong emotional tendency. Researching customers' emotional tendencies can not only measure custo...

AI Technical Summary

Problems solved by technology

Technical methods (1)-(3) and technical methods (5) are only optimized at the input layer of the model, and then CNN is used for emotion judgment. The input of technical methods (4) and (6) at the input layer of the neural network The relatively simple representation of text emotional information results in low accuracy of text sentiment analysis.

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