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Optimal interval distribution ridge regression-based network emotion classification method

A sentiment classification, ridge regression technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of manual labeling, marking noise sensitivity, noise, etc., to achieve a good classification effect, overcome the effect of noise problems

Active Publication Date: 2016-12-07
NANJING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Each web page object in the training object library needs a category mark representing its emotional category, and these marks usually need to be manually marked, because the text and pictures of the web page may not be able to accurately judge the user's emotion in many cases, so these marks May contain some noise
Traditional large interval-based classification methods like Support Vector Machine (SVM) are sensitive to labeled noise because only the interval of a single sample is considered

Method used

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  • Optimal interval distribution ridge regression-based network emotion classification method
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  • Optimal interval distribution ridge regression-based network emotion classification method

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Embodiment Construction

[0019] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0020] Such as figure 1 As shown, the ridge regression network sentiment classification method based on the optimal interval distribution: first, the user prepares a webpage object library containing network sentiment information, and provides a category label for each webpage object in the webpage object library by manual labeling , we refer to the object library with category labels as the webpage training object library. Then, through the feature extraction algorithm, the objects in the traini...

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Abstract

The invention discloses an optimal interval distribution ridge regression-based network emotion classification method. According to the method, network emotion classification problems are described into a form of a convex quadratic optimization object to solve through considering a local in-class relationship and a global inter-class relationship. According to the own features of webpage objects, the invention provides a linear solution and a nonlinear solution, so that the users can carry out self-selection according to practical situations. Moreover, the users can design a proportion of an in-class interval variance to an inter-class interval variance according to requirements, so that the webpage features and the requirements can be better combined.

Description

technical field [0001] The invention relates to a network emotion classification method based on optimal interval distribution ridge regression, and belongs to the technical field of computer artificial intelligence data recognition. Background technique [0002] Internet traces of Internet users to a certain extent carry emotional information that reflects the user's emotions about a certain thing, and this emotional information is called network emotional information. Network emotional information is spread or stored in the network with digital text, picture, video or sound as the carrier. Sometimes it is of great use value. More accurate product recommendation and user customization. In order to analyze the user's network emotional information, it is usually necessary to classify a large number of web pages containing user emotional information. Obviously, it is very time-consuming and labor-intensive to classify only by manpower, but automatically classifying these web...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/285G06F18/24765G06F18/214G06F18/2451G06F18/2453
Inventor 高尉周志华陈加略
Owner NANJING UNIV