<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the rapid dissemination of online public opinion, its emotions are easily transmitted to the general public. It possesses a certain level of social mobilization capacity and can impact the stability of society. To characterize the sentiment trend of social network information and determine its influence, we have proposed a method based on long short-term memory (LSTM) networks and expectation maximization(EM). This model employs a long short-term memory network for data training, obtaining the number of positive public opinions through word-to-word matching. Based on the expectation-maximization method and Jensen’s inequality, the lower bound of the maximum likelihood function is iteratively computed, ultimately achieving convergence of this likelihood function. This convergence value is then used for sentiment analysis. Our study utilizes 10,000 valid pieces of data from the Sina microblog as experimental data. By comparing our model with the K-MEANS model and the EM model, the results indicate significant improvements in the accuracy and convergence of our model. Our research discovers that the influence of public opinion increases as the compensation value for adoption rises, and the probability of public opinion generation gradually increases with the length of user registration years, eventually slowing down.</p>
</abstract>

With the rapid dissemination of online public opinion, its emotions are easily transmitted to the general public. It possesses a certain level of social mobilization capacity and can impact the stability of society. To characterize the sentiment trend of social network information and determine its influence, we have proposed a method based on long short-term memory (LSTM) networks and expectation maximization(EM). This model employs a long short-term memory network for data training, obtaining the number of positive public opinions through word-to-word matching. Based on the expectation-maximization method and Jensen’s inequality, the lower bound of the maximum likelihood function is iteratively computed, ultimately achieving convergence of this likelihood function. This convergence value is then used for sentiment analysis. Our study utilizes 10,000 valid pieces of data from the Sina microblog as experimental data. By comparing our model with the K-MEANS model and the EM model, the results indicate significant improvements in the accuracy and convergence of our model. Our research discovers that the influence of public opinion increases as the compensation value for adoption rises, and the probability of public opinion generation gradually increases with the length of user registration years, eventually slowing down.

Online Public Opinion Analysis Model Based on Long Short-term Memory Network and Expectation Maximization

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the rapid dissemination of online public opinion, its emotions are easily transmitted to the general public. It possesses a certain level of social...

Online Public Opinion Analysis Model Based on Long Short-term Memory Network and Expectation Maximization

Online veröffentlicht: 10. Apr. 2024

Seitenbereich: -

Eingereicht: 26. Jan. 2024

Akzeptiert: 25. Feb. 2024

DOI: https://doi.org/10.2478/amns-2024-0739

Schlüsselwörter
Social networks, public opinion, maximum likelihood, expectation maximization, long short-term memory network

© 2024 Chunliang Zhou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Online Public Opinion Analysis Model Based on Long Short-term Memory Network and Expectation Maximization

Online veröffentlicht: 10. Apr. 2024

Seitenbereich: -

Eingereicht: 26. Jan. 2024

Akzeptiert: 25. Feb. 2024

DOI: https://doi.org/10.2478/amns-2024-0739

SchlüsselwörterSocial networks, public opinion, maximum likelihood, expectation maximization, long short-term memory network

© 2024 Chunliang Zhou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Social networks, public opinion, maximum likelihood, expectation maximization, long short-term memory network