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Network information analysis method based on privacy grouping and emotion recognition

A technology of emotion recognition and network information, applied in biological neural network models, digital data information retrieval, special data processing applications, etc., can solve problems such as loss, poor sensitivity of similarity, and unconsidered differences

Active Publication Date: 2021-03-09
深圳市索迪统计科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the large amount of privacy of individuals, companies, and social groups in big data information, if relevant protection measures are not taken during the release of big data information, sensitive information may be leaked, causing unexpected losses.
[0003] Existing technologies include training recognition methods similar to dual-modal emotion recognition models and private data classification methods based on big data. Among them, dynamic big data requires the application of appropriate noise addition mechanisms. Make protected data useless
Most of the published related methods for dynamic big data release are based on the sliding window model to process updated data, but they are less sensitive to the similarity between measurement and incremental data.
Finally, the existing methods do not consider the possibility of differences between the users trained in the offline phase and the users identified in the online phase.
Since the same information released by different users will not be completely consistent, and this inconsistency is more obvious in channel information, so it directly affects the accuracy of recognition
Most of the above methods use a single method to solve the big data analysis problem of artificial intelligence. Whether it is the interpretability, effectiveness or applicability of the algorithm model, there is still room for improvement

Method used

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  • Network information analysis method based on privacy grouping and emotion recognition
  • Network information analysis method based on privacy grouping and emotion recognition
  • Network information analysis method based on privacy grouping and emotion recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0118] combine figure 1 , taking CSI data image processing release as an example.

[0119] First, the original CSI data of the wireless signal is collected from the receiver, followed by data preprocessing, feature extraction and recognition. In the stage of collecting big data, arrange the transmitter and receiver at any position, and the user performs action collection between the transmitter and the receiver. The transmitter uses a wireless router, the receiver uses a network card and is connected to an external computer, and the sampling frequency is set to 1000Hz. Use the receiver to obtain channel state information. If the obtained local outlier factor LOF value is greater than 1, it will be regarded as outlier removal. Then perform feature extraction. If information on 3 links is received, each link contains 30 subcarriers, and a 30*3 matrix is ​​obtained each time. The sampling rate of the signal is set to 1000 data packets per second, and the contents of the data p...

Embodiment 2

[0121] Described Encoder-Decoder model comprises encoding model and decoding model, and encoding part is the data that length is n, and hidden layer output matrix is

[0122] H=[h 1 ,h 2 ,..., h n ]

[0123] Generate data as v s :

[0124]

[0125] The decoding model is composed of aspect attention modules, and the number of modules N is the same as the total number of aspects in the data set, that is, one aspect attention module corresponds to a specific aspect; when the input data contains multiple aspects, the encoded output H is They are respectively sent to the corresponding aspect attention modules. In each aspect attention module, there is a corresponding aspect information, that is, the aspect vector v ai , aspect vector v ai Splicing with each hidden state in the input matrix H, then performing attention calculations, and finally obtaining data about specific aspects through weighted averaging:

[0126] e tif =tanh(W aif [h t ,v aif ]+b aif )

[0127] ...

Embodiment 3

[0131] Similar to Examples 1 and 2, the difference is that the emotional polarity classification includes: the v s Send it to the full connection layer, and output the probability of the sample to be classified on each emotion category through the softmax function, and get the emotion polarity corresponding to different aspects:

[0132]

[0133] where P tif is the weight matrix of the fully connected layer, b pif is the bias item of the full link layer, and C is the number of categories.

[0134] In order to verify the results of the present invention, the F1 value index is selected to carry out comparative experiments. The F1 value is the harmonic mean of the precision rate and the recall rate, namely:

[0135]

[0136] The present invention selects 6 classic models for three-category comparison, and the verification results are shown in Table 1. It can be seen from the comparison results that the present invention improves the F2 values ​​of negative classificatio...

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Abstract

The invention belongs to the field of artificial intelligence big data analysis, and particularly relates to a network information analysis method based on privacy grouping and emotion recognition. The method comprises the following steps: inputting preprocessed big data channel information into a bidirectional long-short-term memory network, and performing feature extraction to obtain a feature vector; carrying out secondary training on the feature vector to obtain an Encoder-Decoder model based on a multiple attention model; preprocessing the collected big data set to be identified to obtainan identified big data set and the like. According to the method, the bidirectional long-short term memory network is used for feature extraction, meanwhile, the whole model is optimized, the methodof combining the Encoder-Decoder model based on the multiple attention models and the bidirectional long-short term memory network is provided, the duration is relatively shorter, the prediction speedis relatively higher, the convergence speed is high, and the recognition accuracy is high. The accuracy of sentiment analysis is improved, and the judged result has higher precision than a traditional sentiment analysis algorithm.

Description

technical field [0001] The invention belongs to the field of artificial intelligence big data analysis, in particular to a network information analysis method based on privacy grouping and emotion recognition. Background technique [0002] With the rapid development of Internet big data and artificial intelligence, various online social methods have penetrated into all aspects of human social life, and various social information is expressed on the Internet, including individual and group attitudes, opinions and emotions. Today, network information has developed from simple browsing and acceptance to various dimensions and has formed a scale of big data level. The methods of information recognition include speech recognition, action recognition, character recognition, music recognition, password recognition and channel information recognition. For big data technology and artificial intelligence technology, a big problem lies in the automatic processing and analysis of infor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/62G06F16/215G06N3/04
CPCG06F21/6245G06F16/215G06N3/049
Inventor 不公告发明人
Owner 深圳市索迪统计科技有限公司