Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model  

作　　者：Yasser Alharbi Sarwar Shah Khan 

机构地区：[1]College of Computer Science and Engineering,University of Hail,Hail,55436,Saudi Arabia [2]Department of Computer Science,University of Engineering&Technology,Mardan,23200,Pakistan

出　　处：《Computers, Materials & Continua》2025年第4期1275-1290,共16页计算机、材料和连续体(英文)

摘　　要：Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关 键 词：Sentiment analysis stochastic gradient descent REVIEWS English IMDb dataset Arabic dataset 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]