Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects.Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy.The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms.Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process.In unsupervised mechanisms,a lexicon is constructed for storing polarity terms.The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms.In addition,most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process.Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process.This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research.A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms.An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms.An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically.The experimental results are conducted on two datasets to measure the over-all accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

Dynamic modeling and aperiodically intermittent strategy for adaptive finite-time synchronization control of the multi-weighted complex transportation networks with multiple delays

The idea of network splitting according to time delay and weight is introduced.Based on the cyber physical systems(CPS),a class of multi-weighted complex transportation networks with multiple delays is modeled.The finite-time synchronization of the proposed complex transportation networks model is studied systematically.On the basis of the theory of stability,the technique of adaptive control,aperiodically intermittent control and finite-time control,the aperiodically intermittent adaptive finite-time synchronization controller is designed.The controller designed in this paper is beneficial for understanding the synchronization in multi-weighted complex transportation networks with multiple delays.In addition,the conditions for the existence of finite time synchronization have been discussed in detail.And the specific value of the settling finite time for synchronization is obtained.Moreover,the outer coupling configuration matrices are not required to be irreducible or symmetric.Finally,simulation results of the finite-time synchronization problem are given to illustrate the correctness of the results obtained.

Partial Topology Identification of Stochastic Multi-Weighted Complex Networks Based on Graph-Theoretic Method and Adaptive Synchronization

This article aims to identify the partial topological structures of delayed complex network.Based on the drive-response concept,a more universal model,which includes nonlinear couplings,stochastic perturbations and multi-weights,is considered into drive-response networks.Different from previous methods,we obtain identification criteria by combining graph-theoretic method and adaptive synchronization.After that,the partial topological structures of stochastic multi-weighted complex networks with or without time delays can be identified successfully.Moreover,response network can reach synchronization with drive network.Ultimately,the effectiveness of the proposed theoretical results is validated through numerical simulations.