A text sentiment analysis method based on a bidirectional long-short term memory neural network

A technology of long-short-term memory and neural network, applied in the field of text sentiment analysis based on two-way long-short-term memory neural network, can solve the problems of inability to extract text features, lack of ability to learn sequential sequences, etc., to speed up training and reduce The effect of quantity and efficient classification

Active Publication Date: 2019-04-09
CHONGQING UNIV OF POSTS & TELECOMM
View PDF5 Cites 34 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Using a single deep learning model will have the following disadvantages: (1) CNN can acquire the ability to capture local features, but lack

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
  • A text sentiment analysis method based on a bidirectional long-short term memory neural network
  • A text sentiment analysis method based on a bidirectional long-short term memory neural network
  • A text sentiment analysis method based on a bidirectional long-short term memory neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034]In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, and Not all examples.

[0035] The present invention mainly includes two parts: data feature extraction and model training. The data feature extraction is to construct the framework on two levels: the word vector mapping layer and the convolution extraction layer. The training of the model is responsible for analyzing the extracted data, and at the same time adjusting the parameters of the network to adapt to different data and achieve the effect of model training. The features in the text are extracted through the convolutional network, and then the extracted features are recombined and sent to the cyclic neural networ...

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 belongs to the crossing field of artificial intelligence and data mining, and particularly relates to a text sentiment analysis method based on a bidirectional long-short term memory neural network. The method comprises the following steps: in a text mapping word vector mapping framework, expressing a text as a word vector matrix; Constructing an improved convolutional neural network, and performing feature extraction on the mapped word vector matrix; Training the extracted features by using a bidirectional long-short term memory recurrent neural network until the loss function is minimum; And adding a global mean pooling layer and a classification layer behind the recurrent neural network so as to output sentiment classification of the text. According to the method, deep learning is applied to text sentiment analysis, the limitation of manual extraction of text sentiment characteristics is avoided, the advantages of a convolutional neural network and a recurrent neural network are combined in the field of deep learning, and the network is improved, so that the method can be better applied to a text processing task.

Description

technical field [0001] The invention belongs to the intersecting field of artificial intelligence and data mining, and in particular relates to a text emotion analysis method based on a bidirectional long-short-term memory neural network. Background technique [0002] In recent years, deep learning has been more and more widely used in text classification processing. Text classification aims to automatically process text information, so as to quickly and accurately analyze massive texts. Due to the rapid development of the Internet, more and more subjective information appears on the Internet, so it is of great significance to conduct sentiment analysis on users' subjective information to grasp the user's value trend. As an important branch of text classification, sentiment analysis has become a research hotspot in the fields of natural language processing, data mining, and information retrieval. [0003] Traditional text sentiment analysis methods mainly use the Bag of Wo...

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
IPC IPC(8): G06F16/35
CPCY02D10/00
Inventor 徐光侠潘霖马创张业吴佳健袁野周代棋郑爽
Owner CHONGQING UNIV OF POSTS & TELECOMM
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