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Method for establishing sentiment classification model

A technology for learning models and hierarchical structures, applied in the field of information machine learning, to solve problems such as lack of causality

Inactive Publication Date: 2014-04-16
BEIJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

Such techniques lack representations of causality and may face challenges in obtaining abstract concepts such as "sibling relationship" or "co-reference"

Method used

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  • Method for establishing sentiment classification model
  • Method for establishing sentiment classification model
  • Method for establishing sentiment classification model

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Embodiment Construction

[0025] In order to make the object, technical solution and advantages of the present invention clearer, the solutions of the present invention will be further described in detail below with reference to the accompanying drawings and examples.

[0026] X={x in a given review corpus 1 ,x 2 ,...,x N}, the task is to sentiment classify these documents, that is, to give each comment its attribute (positive or negative). where each comment is denoted as x i ={x i3 ,x i2 ,...,x iD}. At the same time, the target attribute is also represented by a vector as y i ={1,-1}, where 1 is positive and -1 is negative. Suppose the target value vector set is Y={y 1 ,y 2 ,...,y N}, the purpose is to find out the mapping function from X to Y.

[0027] We use the deep belief network as a deep model to train comment prediction. The deep belief network is a multi-layer model, with an input layer as the visible layer and multiple hidden layers such as figure 1 , we set the following parame...

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Abstract

The invention provides a sentiment classification method for generating a model deep-convinced-degree network on the basis of the probability of depth study. According to the technical scheme of the method, a plurality of Boltzmann machine layers are stacked, namely, output of this layer is used as input of the next layer. By the adoption of the mode, input information can be expressed in a grading mode, and abstraction can be conducted layer by layer. A multi-layer sensor containing a plurality of hidden layers is the basic study structure of the method. More abstract high layers are formed through combining the characteristics of lower layers and are used for expressing attribute categories or characteristics, so that the distribution type character presentation of data can be discovered. The method belongs to monitoring-free study, and a mainly-used model is the deep-convinced-degree network. The method enables a machine to conduct characteristic abstract better so as to improve the accuracy of sentiment classifications.

Description

technical field [0001] This application relates to the field of information machine learning, and specifically designs a method for constructing a probability generation model. Background technique [0002] With the rise of today's Internet age, the front page of the "New York Times" called deep learning a revolutionary new technology for artificial intelligence. There is reason to take our understanding of deep learning a step further, as a complex "machine learning" algorithm that far outperforms previous techniques in recognizing audio and images with accuracy. But there are also good reasons to be skeptical of this notion. Although it is reported that "deep learning allows machines to perform human activities, such as seeing, listening and thinking, pattern recognition provides the possibility and promotes the progress of artificial intelligence technology." Deep learning allows us to move towards the era of real intelligent machines , is only a small step. Combined w...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/35G06N3/08
Inventor 周延泉
Owner BEIJING UNIV OF POSTS & TELECOMM
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