Text feature judgment method, device and storage medium based on neural network

Abstract

The embodiment of the present invention discloses a neural network-based text feature judgment method, device, and storage medium. The method includes assigning identification information to identification characters and generating character identification associated data. The identification characters include Chinese characters, punctuation characters, and numbers. At least one of characters and English characters; obtain a text training set, the text set includes a plurality of training texts and the corresponding training text features of each training text, each of the training texts is composed of one or more of the identification characters Composition; generate a curve coordinate diagram corresponding to each of the training texts according to the character identification associated data; use the curve coordinate diagram as input, and use the neural network as an output to train the text feature judgment template; The text to be recognized is acquired, and the text to be recognized is judged according to the text feature judgment template to determine the text feature. This solution realizes the in-depth analysis of the text content, and the obtained text features are highly accurate, and at the same time, the judgment efficiency is improved.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technologies, and in particular, to a method, device, and storage medium for judging text features based on a neural network. Background technique [0002] With the development of social networks, e-commerce, mobile Internet and other technologies, blogs, forums, and social service networks such as Dianping have generated a large number of user participation, which are valuable to people, events, products, etc. with emotional colors. Text comment data information, which is rapidly expanding, expresses people's various emotional colors and emotional tendencies, such as joy, anger, sadness, joy, criticism, praise, etc. Fully mining and in-depth analysis of such textual comment data information can better understand the opinions and positions of netizens, so as to better assist decision-making in various fields such as public opinion management and control, business decision-making...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, when the text is processed to judge the text features (such as the emotional features represented by the text), the accuracy is low, and the judgment effect and judgment efficiency are poor

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