A text sentiment analysis method combining BiLSTM with an Attention mechanism

A mechanism and text technology, applied in the field of natural language processing technology and text sentiment analysis, can solve the problems of model dimension disaster, high dimension difficult to train, etc.

Pending Publication Date: 2019-05-10
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

Aiming at the high-dimensionality of data representation in text classification is difficult to train and the vector representation feature is irrelevant, the text data is mapped to a low-dimensional real number vector to avoid the problem of high-dimensional input causing the model to produce dimension disasters

Method used

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  • A text sentiment analysis method combining BiLSTM with an Attention mechanism
  • A text sentiment analysis method combining BiLSTM with an Attention mechanism
  • A text sentiment analysis method combining BiLSTM with an Attention mechanism

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Embodiment Construction

[0050] Further description will be made below in conjunction with the accompanying drawings and the experimental results carried out using the model proposed by the present invention.

[0051] figure 1It is a text sentiment analysis research framework based on deep learning designed by the present invention, and the specific research process is:

[0052] Step 1: Process the data

[0053] Since the data set contains some data that is irrelevant to model training, the read data is first processed to clean unnecessary characters such as line breaks and quotation marks, and at the same time convert uppercase letters to lowercase letters for better Process the eigenvectors.

[0054] Step 2: Divide the data set into a test set and a training set by 8:2

[0055] Step 3: Train word vectors

[0056] Step 3.1: Serialize the data

[0057] If the traditional sparse representation is used to represent words, it will cause the curse of dimensionality when solving this problem. Therefo...

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Abstract

The invention provides a short text sentiment tendency analysis method combining BiLSTM with an Attention mechanism. A model combining a convolutional bidirectional long-short time memory network withan attention mechanism is constructed; for the problems that high dimensions represented by data in text classification are difficult to train and vector representation features are irrelevant, the text data are mapped to a low-dimension real number vector, and the problem that a model is subjected to dimension disasters due to high-dimension input is avoided; and for the feature selection problem, constructing a feature extraction model, and carrying out text sentiment classification on the extracted features.

Description

technical field [0001] This patent belongs to the field of natural language processing technology and text sentiment analysis technology. It provides a method of short text sentiment analysis combined with BiLSTM and Attention mechanism; it aims to build a text sentiment analysis model and improve the classification accuracy of the system. Background technique [0002] In just a few decades, the development of the Internet has proved to people that its appearance is the greatest invention in human history, and now its application has penetrated into all aspects of people's daily life and work. With the development of social media such as Weibo, BBS, Douban, and WeChat, people's behavior on the Internet is no longer limited to browsing information, and more people begin to express their opinions, share knowledge, and create content on social networks. . These contents include not only comments on hot news events, but also comments on specific commodities, all of which contai...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06N3/04G06N3/08G06K9/62
Inventor 司新红王勇
Owner BEIJING UNIV OF TECH
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