Text emotion recognition method based on maximum probability filling and multi-head attention mechanism
A technology with maximum probability and attention, applied in character and pattern recognition, neural learning methods, computer components, etc., can solve problems such as data sparseness and semantic confusion
Pending Publication Date: 2022-07-12
HOHAI UNIV
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However, due to the different lengths of the input text, it is necessary to fill the matrix to make the length consistent during batch operations. The mainstream filling methods such as zero-filling and looping will cause data sparseness and semantic confusion.
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[0054] Below in conjunction with specific embodiments, the present invention will be further illustrated, and it should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. The modifications all fall within the scope defined by the appended claims of this application.
[0055] like figure 1 As shown, the text sentiment classification method based on LDA maximum probability filling and multi-head attention mechanism includes the following steps:
[0056] Step S10: Acquire a text data set, and perform text preprocessing. The data set is divided into three parts: training set, validation set and test set according to the ratio of 8:1:1. Each document has a corresponding sentiment polarity label: 0 or 1, representing positive sentiment and negative sentiment, respectively; positive and negative The samples are evenly distributed. Text preprocessing can make the text conform to the input forma...
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The invention discloses a text emotion recognition method based on LDA maximum probability filling and a multi-head attention mechanism, and the method comprises the following steps: (1) obtaining a text data set, and carrying out the text preprocessing; (2) obtaining a dictionary and a word vector matrix through a Word2Vec model; (3) filling a word vector matrix by using an LDA maximum probability method; and (4) mining local features of the text through a Text-CNN network convolution operation, carrying out weight redistribution through a multi-head attention mechanism, and finally obtaining a classification probability of emotional polarity through Softmax. The method can better adapt to the classification task of the network emotion text with flexible length, not only can fully solve the problems of short text data sparseness and filling, but also can cope with the difficulty of long text information extraction.
Description
technical field [0001] The invention relates to a text emotion classification method based on LDA maximum probability filling and multi-head attention mechanism, and belongs to the technical field of text data recognition. Background technique [0002] With the development of Internet technology and the arrival of the era of big data, people are more inclined to express their true opinions on online platforms, and the amount of emotional information such as instant chat, current affairs reviews, and product reviews has grown rapidly. The sentiment classification of these texts has high application value, and can provide support for decision-making judgment, public opinion guidance, recommendation system and other directions. At the same time, these online platform texts will have the characteristics of flexible length and simple grammatical structure, which greatly increases the difficulty of discrimination. Therefore, obtaining short text feature information and improving ...
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IPC IPC(8): G06F40/284G06F40/289G06F40/242G06K9/62G06N3/04G06N3/08
CPCG06F40/284G06F40/242G06F40/289G06N3/08G06N3/047G06N3/045G06F18/2415
Inventor 戴梦瑶朱李玥刘文强柏雪嫣邢莉娟
Owner HOHAI UNIV