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Text sentiment classification method and device

A sentiment classification, text technology, applied in the computer field, can solve problems such as poor sentiment classification effect

Pending Publication Date: 2021-04-16
BEIJING GRIDSUM TECH CO LTD
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AI Technical Summary

Problems solved by technology

The current classic text sentiment classification methods usually use deep learning models, and deep learning models require a large number of labeled samples as training data to obtain the final model for sentiment classification, and the sentiment classification effect is not good

Method used

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  • Text sentiment classification method and device
  • Text sentiment classification method and device
  • Text sentiment classification method and device

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Embodiment Construction

[0053]In recent years, language models based on pre-training have been open-sourced, such as BERT (Bidirectional Encoder Representation from Transformers) models, GPT models, etc. These language models have been pre-trained through a large amount of original text, so they can be obtained with less data labeling Great mockup effect. However, these language models cannot obtain the position information of words, that is, they cannot let the model know the objects that need to be classified for sentiment. Therefore, these language models are only suitable for simple text classification, and cannot be used for text emotions with word positions of sentiment classification attributes. Classification. These language models are pre-trained general-purpose language models, and only need to continue training with a small amount of domain-specific corpus to obtain a language model suitable for analyzing text in a specific domain.

[0054] Obtaining the vector matrix corresponding to the...

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Abstract

The invention provides a text sentiment classification method and device. A text coding model belonging to the same field as a to-be-classified text is used for analyzing context semantic information in the to-be-classified text in an omni-directional mode to obtain a corresponding text vector; meanwhile, a position vector of the sentiment classification attribute word is acquired, and then the text vector and the position vector are spliced to obtain a text and a position vector. The text and the position vector contain context semantic information of to-be-classified text and position information of the emotion classification attribute words, and the target emotion classification model can define an emotion analysis object according to the position information of the emotion classification attribute words, so that the accuracy of an emotion analysis result is improved. Moreover, the text vector can more accurately represent the semantic information of the text to be classified, so that the emotion classification model can better understand the context information of the text, and the classification accuracy is further improved. In addition, the data annotation quantity can be greatly reduced by adopting the pre-trained text coding model.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a text sentiment classification method and device. Background technique [0002] Text sentiment classification refers to the sentiment classification of brand words or attribute words in the text. The current classic text sentiment classification methods usually use deep learning models, but deep learning models require a large number of labeled samples as training data to obtain the final model for sentiment classification, and the sentiment classification effect is not good. Contents of the invention [0003] In view of this, the object of the present invention is to provide a text sentiment classification method and device to reduce the amount of labeled data required for training and shorten the training cycle. The specific technical solutions are as follows: [0004] In a first aspect, the present invention provides a text sentiment classification method, co...

Claims

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Application Information

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IPC IPC(8): G06F16/35G06F40/289G06F40/30G06K9/62
Inventor 戴泽辉
Owner BEIJING GRIDSUM TECH CO LTD
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