Long text sentiment analysis method fusing Attention mechanism

A technology of sentiment analysis and long text, applied in computer parts, special data processing applications, instruments, etc., has limited ability to solve contextual information, cannot effectively solve long-range dependence problems, future information modeling and other problems, and achieves reduction effect of complexity

Inactive Publication Date: 2018-09-28
FUZHOU UNIV
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AI Technical Summary

Problems solved by technology

[0003] One-way neural networks are usually used to analyze short text comment data in social networks. Since one-way neural networks consider historical and current information, they cannot model future informati

Method used

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  • Long text sentiment analysis method fusing Attention mechanism
  • Long text sentiment analysis method fusing Attention mechanism
  • Long text sentiment analysis method fusing Attention mechanism

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] Please refer to figure 1 and figure 2 , the present invention provides a kind of long text emotion analysis method that integrates into Attention mechanism, it is characterized in that:

[0043] Step S1: Initialize the words in the text with word vectors, map the text into a set of word vectors, and use the word vectors as the input of the network and input them to the hidden layer;

[0044] Step S2: Introduce the GRU unit in the hidden layer to calculate the hidden state, increase the reverse network, and obtain the information of its context for each word in the sentence; specifically include:

[0045] Using bidirectional gate cycle unit modeling, the update formula of the GRU unit is as follows:

[0046] update gate z t The formula for calculating:

[0047] z t =σ(W z x t +U z h t-1 ) (1)

[0048] where x t is the input word vecto...

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Abstract

The invention relates to a long text sentiment analysis method fusing an Attention mechanism. A text sentiment classification model (Bi-Attention model) is built by adopting a bidirectional thresholdrecurrent neural network in combination with the Attention mechanism. Attention can enable a neural network to pay attention to important information in a text and ignore or reduce the influence of secondary information in the text, thereby lowering the complexity of long text processing. On the other hand, a context vector is generated through a bidirectional threshold recurrent unit, and a memory state is updated, thereby fully considering the influence of historical information and future information on semantics.

Description

technical field [0001] The invention relates to a long text sentiment analysis method integrated into the Attention mechanism Background technique [0002] Text sentiment analysis (also known as opinion mining) is the use of natural language processing, text mining and computer linguistics to identify and extract subjective information in the original material. These subjective texts are increasing at an exponential rate every day. Using computers to automatically analyze the emotions expressed in these subjective texts has become a hot spot in the academic circles. At present, most of the comment data in social networks are short text data, because longer sequence data or chapter data may contain rich emotional information, and may also contain information that is irrelevant to the current sentiment analysis. [0003] One-way neural networks are usually used to analyze short text comment data in social networks. Since one-way neural networks consider historical and current...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/28G06K9/62G06N3/04
CPCG06N3/04G06F40/58G06F18/2411
Inventor 郑相涵郑文妃
Owner FUZHOU UNIV
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