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

Intelligent Automation & Soft Computing 2023年第1期35卷 1015-1034页

作者： Ayman Mohamed Mostafa College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia

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.

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

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