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Classifier training method, classifier and sentiment classification system

A training method and technology for training systems, applied in the fields of classifiers, sentiment classification systems, and classifier training methods, can solve problems such as increasing the number of users and increasing the workload of users, and achieve the effect of improving accuracy and classification accuracy.

Inactive Publication Date: 2016-09-07
SUZHOU UNIV
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  • Abstract
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  • Claims
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Problems solved by technology

At the present stage, classifiers are usually used to classify these comment texts. When training the classifier, usually only a large number of marked comment texts are used to train the classifier, because obtaining these labels may require It consumes a lot of manpower and material resources. If a classifier with higher accuracy is to be obtained, the number of marked comment texts used in the training process needs to be greatly increased, thereby greatly increasing the user's workload. Therefore, How to improve the accuracy of the classifier without greatly increasing the workload of users has become one of the technical problems to be solved urgently by those skilled in the art

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  • Classifier training method, classifier and sentiment classification system
  • Classifier training method, classifier and sentiment classification system
  • Classifier training method, classifier and sentiment classification system

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Embodiment Construction

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0037] For ease of description, the commonly used terms and signs involved in the present invention are introduced as follows:

[0038] At present, the research on sentiment analysis basically draws on machine learning methods such as text classification, and has not yet formed a set of independent research methods according to its own characteristics. Of course, sentiment analysis can also be seen as a special text classification to a certain extent. The more...

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Abstract

The invention provides a classifier training method, a classifier and a sentiment classification system. The method comprises the steps of obtaining a labeled comment text of at least one topic in a topic set as a training sample; obtaining an unlabeled comment text of at least one topic in the topic set as an unlabeled sample; predicting the unlabeled sample by adopting a label propagation algorithm; and training the classifier by adopting the predicted unlabeled sample and the training sample. According to the scheme, the classifier is trained by utilizing the labeled comment text and the unlabeled comment text, and after the classifier is trained by adopting the unlabeled comment text, the classification precision of the classifier is remarkably improved; and on the basis of not remarkably increasing the workload of a user, the accuracy of the classifier is effectively improved.

Description

technical field [0001] The invention relates to the technical field of natural language processing and pattern recognition, in particular to a classifier training method, a classifier and an emotion classification system. Background technique [0002] With the continuous improvement of the status of the Internet in users' lives, shopping, reading news, reading books, and watching film and television information through the Internet have become one of the most important parts of users' online life. Make subjective comments on the browsing content. There are positive comments in these comment texts, and of course there are negative comment texts. Other users can determine the browsability of the browsing content corresponding to these comment texts through the content of these comment texts. For example, the positive comments in these comment texts If there are many comments, it can be considered that the browseability of the browsing content is high, and if there are many neg...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/355G06F16/9574G06F18/241
Inventor 李寿山张栋周国栋贡正仙
Owner SUZHOU UNIV