Deep learning multi-classification emotion analysis model combined with attention mechanism

A technology of deep learning and sentiment analysis, applied in neural learning methods, biological neural network models, text database clustering/classification, etc., can solve problems such as performance limitations

Active Publication Date: 2019-09-27
BEIJING UNIV OF TECH
View PDF3 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the sample data is long or the language scene is complex, the intervals of useful emotional information vary in size and length, and the performance of Long Short-Term Memory (LSTM) is therefore limited.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep learning multi-classification emotion analysis model combined with attention mechanism
  • Deep learning multi-classification emotion analysis model combined with attention mechanism
  • Deep learning multi-classification emotion analysis model combined with attention mechanism

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0058] The specific implementation of the present invention will be further described in detail below in conjunction with the diagrams and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0059] The method that the present invention proposes is to realize by following steps successively:

[0060] Step (1) data preprocessing

[0061] The emotional language data set is expressed as: G=[(segtxt 1 ,y 1 ),(segtxt 2 ,y 2 ),...,(segtxt N ,y N )], where segtxt i Indicates the i-th sample, y i is the corresponding sentiment category label. N represents the number of samples in the data set G, and the emotion labels are divided into four categories: "joy", "anger", "disgust", and "depression". N is 80,000, and each of the four types of emotion samples is 20,000. Data preprocessing for samples in G includes the following steps:

[0062] 1) Word segmentation, deactivation, uppercas...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a deep learning multi-classification emotion analysis model combined with an attention mechanism, belongs to the technical field of natural language processing. The invention analyzes weakness of an existing CNN network and an existing LSTM network in the aspect of text emotion analysis, and provides the deep learning multi-classification emotion analysis model combined with the attention mechanism. According to the model, local features extracted by a CNN network and word order features extracted by an LSTM model are fused by using the attention mechanism, and emotion features extracted by the CNN network and the LSTM network are spliced on a classification layer by using an idea of an integrated model to serve as emotion features finally extracted by the model. Through a contrast experiment, the accuracy of the model is obviously improved.

Description

technical field [0001] The invention belongs to the field of text information processing, and relates to a deep learning multi-category sentiment analysis model combined with an attention mechanism. Background technique [0002] With the continuous rise of social networks such as Weibo and Twitter, the Internet is not only a source for people to obtain daily information, but also an indispensable platform for people to express their opinions. People comment on hot events in online communities, express opinions on film reviews, and describe product experience, etc., which will generate a large amount of text information with emotional color (such as: emotions, etc.), and effective sentiment analysis of these text information can be more effective. Get a better understanding of the user's interest tendencies and attention levels. However, with the increase of people's attention to network information, a large number of texts with emotional color are produced in the network co...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/35G06F17/27G06N3/08
CPCG06F16/35G06N3/084G06F40/30
Inventor 刘磊孙应红陈浩李静
Owner BEIJING UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap