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

作     者：Ayman Mohamed Mostafa 

作者机构：College of Computer and Information SciencesJouf UniversitySakaka72314Saudi Arabia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第1期

页      面：1015-1034页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：funded by the Deanship of Scientific Research at Jouf University under Grant No.(DSR-2021-02-0102) 

主　　题：Sentiment analysis semi-supervised framework multi-weight polarity algorithm Arabic lexicons and automated scaling algorithm 

摘      要：Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and *** methods and techniques have been introduced to analyze sentiments for obtaining high *** sentiment analysis accuracy depends mainly on supervised and unsupervised *** mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming *** unsupervised mechanisms,a lexicon is constructed for storing polarity *** accuracy of analyzing data is considered moderate or low if the lexicon contains small *** addition,most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis *** both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging *** 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 *** interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic *** enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities *** 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.