Method for judging text emotion tendency based on recurrent neural network

A technology of cyclic neural network and emotional tendency, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve problems such as unconsidered language rules and integration, achieve good versatility and improve accuracy

Pending Publication Date: 2020-11-03
JIANGSU UNIV
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Problems solved by technology

Existing studies have demonstrated that recurrent neural networks are suitable for learning long-term dependencies between sentences, but have not considered incorporating traditional linguistic rules into classification models

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  • Method for judging text emotion tendency based on recurrent neural network
  • Method for judging text emotion tendency based on recurrent neural network
  • Method for judging text emotion tendency based on recurrent neural network

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Embodiment Construction

[0037] The present invention will be further described in detail below in conjunction with the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0038] The invention provides a method for judging the emotional tendency of text based on a recurrent neural network, comprising the following steps:

[0039] S1) Preprocessing the input text, using the word vector table generated by the Continuous bag of Words (CBOW) to obtain the vectorized representation of each word in the input text;

[0040] As a preferred embodiment of the present invention, preprocessing the sentence of the input text includes removing designated useless symbols, keeping the text only in English, word segmentation, and removing stop words.

[0041] As a preferred embodiment of the present invention, such as figure 1 As shown, the implementation of the CBOW model first selects a central word in the text, then defines a sliding window with a length o...

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Abstract

The invention discloses a method for judging text sentiment tendency based on a recurrent neural network, which integrates traditional linguistic rules into an LSTM text classification model by combining the advantages of LSTM in learning text context. After context information of words is learned by using a recurrent neural network, an existing sentence-level sentiment analysis LSTM model is introduced through a loss function. Under the condition that the complexity of the model is not increased, the information of the sentiment dictionary, the negative words and the degree adverbs is effectively utilized, and a good result is obtained on an experimental data set.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a method for judging the emotional tendency of a text based on a recurrent neural network. Background technique [0002] Automatic text classification is a research hotspot and core technology in the field of information retrieval and data mining, which has received extensive attention and rapid development in recent years. Before the 1990s, the automatic classification of texts was mainly carried out by means of knowledge engineering, that is, manual classification by professionals, which has the disadvantages of low efficiency and high cost. Since the 1990s, researchers have realized automatic text classification through machine learning methods, such as support vector machine algorithm SVM, KNN algorithm and Logistic algorithm, etc., and achieved relatively good results. In recent years, with the rapid development of deep learning, it has been ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/31G06F16/33G06F16/35G06F40/289G06N3/04G06N3/08
CPCG06F16/31G06F16/353G06F16/3344G06N3/049G06N3/084G06F40/289G06N3/044G06N3/045
Inventor 刘志锋杨云成周从华
Owner JIANGSU UNIV
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