Optimizing neural networks for public opinion trends prediction

Ye, Xuelian; Yang, Kongyu

doi:10.1109/icnc.2015.7377961

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Experiment results showed that, by combining opposition-based learning method, well-designed onlooker bee search equation and mutation bee phase, EHABC get better exploitation and exploration abilities than the other four ABC variants, and improves original ABC both in terms of accuracy and convergence speed. In the future, the researchers of this paper will extend the research for the aim of applying EHABC to practical applications, such as public opinion trends prediction problem [20].…”

Section: Discussionmentioning

confidence: 99%

An enhanced hybridized artificial bee colony algorithm for optimization problems

Huang

Zeng

Han

et al. 2019

IJ-AI

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Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

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Section: Discussionmentioning

confidence: 99%

An enhanced hybridized artificial bee colony algorithm for optimization problems

Huang

Zeng

Han

et al. 2019

IJ-AI

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“…GA is a computational model that simulates the genetic mechanisms of natural selection and biological evolution. It’s a way to search the optimization scheme by simulating natural evolutionary processes [24] , [20] . GA is a global optimization search algorithm.…”

Section: Methodsmentioning

confidence: 99%

Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

2021

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The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion. INDEX TERMS Major emergencies, internet public opinion, Early warning index system, COVID-19, CRITIC, GA-BP neural network.

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“…The Metropolis acceptance criterion was implemented with the aim of improving the local searching ability of GA. Likewise, Ye et al [28] combined GA and simulated annealing algorithm to optimize the initial weights to be trained by a BP-based neural network of one hidden layer to predict opinion trends in Chinese texts.…”

Section: Related Workmentioning

confidence: 99%

A Deep Neural Network Optimized by a Genetic Algorithm to Improve Arabic Sentiment Classification

Alharbi¹,

Hamed²,

Alzoubi³

2023

ISI

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Deep learning has improved the state-of-the-art in sentiment analysis for various languages, including Arabic. One aspect that can affect the performance of deep learning-based sentiment classification is the optimization method used for training the neural network. The conventional optimization method is carried out by a backpropagation (BP) algorithm that relies on gradient descent to find the minimum of a cost function. However, BP has the tendency to converge into local minima instead of global minima since neural networks generate complex error surfaces for even simple problems. In this study, for the purpose of improving the Arabic sentiment classification, we propose to use a genetic algorithm (GA) to train a deep neural network (DNN). GA is a meta-heuristic optimization algorithm inspired by the theory of natural evolution. The algorithm is expected to improve the classifier's performance due to its capability to reach optimal or near-optimal solutions. The proposed method uses Arabic sentiment lexicons to extract various features considering different aspects for text representation. The effectiveness of the proposed method is evaluated by analyzing its performance, versus a DNN trained with BP algorithm. The experimental results show that the proposed method can present better F1-measure of 90.7% for Arabic sentiment classification than traditional BP-based DNN.

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Optimizing neural networks for public opinion trends prediction

Cited by 4 publications

References 4 publications

An enhanced hybridized artificial bee colony algorithm for optimization problems

An enhanced hybridized artificial bee colony algorithm for optimization problems

Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

A Deep Neural Network Optimized by a Genetic Algorithm to Improve Arabic Sentiment Classification

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