Chinese short-text sentiment classification method based on text characteristic insertion

A technology of sentiment classification and short text, applied in the information field, can solve problems such as improving accuracy, and achieve the effect of improving accuracy, improving network language changes, and satisfying compromises

Active Publication Date: 2016-10-26
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0004] In view of the above-mentioned defects in the prior art, the present invention provides a Chinese short text sentiment classification method based on text

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  • Chinese short-text sentiment classification method based on text characteristic insertion
  • Chinese short-text sentiment classification method based on text characteristic insertion
  • Chinese short-text sentiment classification method based on text characteristic insertion

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Embodiment Construction

[0044] The present invention will be further described below by specific examples.

[0045] figure 1 It is the general process of the text sentiment classification method, that is, firstly, preprocessing and text feature extraction are performed on the training set and the text to be classified respectively, and the characteristics of each text are obtained, then the classifier is trained with the training set text, and finally the trained classifier is used , according to the characteristics of the text to be classified, sentiment classification is performed on the text to be classified. figure 2 It is the basic process of the Chinese short text sentiment classification method based on text feature embedding in the present invention, through figure 1 and figure 2 It can be found that the method proposed by the present invention has been improved and designed mainly from three aspects. On the one hand, the present invention uses feature embedding to extract text features; ...

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Abstract

The invention discloses a Chinese short-text sentiment classification method based on text characteristic insertion. Characteristic extraction is carried out in a text characteristic insertion manner; firstly, words are represented by vectors having a relatively short length through word insertion; on one hand, the characteristic dimension is reduced; on the other hand, the similar relationship between words can be described better; then, word vectors are weighted according to positions of different words in a text; therefore, text characteristic insertion is carried out; text characteristics having relatively low dimension can be obtained; on this basis, Chinese short-text sentiment classification is carried out, so that the Chinese short-text sentiment classification precision is increased; furthermore, because network languages are rapid to change and the randomness of Chinese short-texts is high; the word vectors are continuously updated in an incremental learning manner; therefore, the word vectors can keep up with change of a text to be trained; and thus, the text sentiment classification precision is improved.

Description

technical field [0001] The invention belongs to the field of information technology and relates to a method for extracting Twitter text events. Background technique [0002] With the rapid development of Internet technology and the popularization and mobilization of communication equipment, convenient network applications such as microblogs and online communities have risen rapidly, and more people obtain information on the Internet and express their attitudes and opinions on it. The Internet has gradually developed into the main carrier of information release, acquisition and delivery. Grasp the opinions and emotions expressed by Internet users on the Internet, and accurately evaluate the popularity of products and services to improve the quality of products and services; grasp the attitudes of netizens during the occurrence and development of events, and correctly monitor and guide public opinion; dialysis According to the personal preferences of different Internet users,...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 张胜李沛程佳军丁兆云张鑫王晖沈大勇陈科第叶栋乔凤才
Owner NAT UNIV OF DEFENSE TECH
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